# embeddings_queue

created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 7195a743-b6ba-4885-a38f-db7e4f497ffb
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 5438, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Add a comment\n\u00a0|\u00a0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n            2\n        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdding to Arthur's excellent answer, I want to point out that extracting jamo from hangeul syllables is very straightforward from the standard. While the solution isn't specific to Excel or Access (it's a Python module), it only involves arithmetic expressions so it should be easily translated to other languages. The formulas, as can be seen, are identical to those in page 109 of the standard. The decomposition is returned as a tuple of integers encoded strings, which can be easily verified to correspond to the Hangul Jamo Code Chart.\n# -*- encoding: utf-8 -*-\n\nSBase = 0xAC00\nLBase = 0x1100\nVBase = 0x1161\nTBase = 0x11A7\nSCount = 11172\nLCount = 19\nVCount = 21\nTCount = 28\nNCount = VCount * TCount\n\n\ndef decompose(syllable):\n    global SBase, LBase, VBase, TBase, SCount, LCount, VCount, TCount, NCount"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 2a44532b-4c7b-4999-859c-79499ce46361
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 6330, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "S = ord(syllable)\n    SIndex = S - SBase\n    L = LBase + SIndex / NCount\n    V = VBase + (SIndex % NCount) / TCount\n    T = TBase + SIndex % TCount\n\n    if T == TBase:\n        result = (L,V)\n    else:\n        result = (L,V,T)\n\n    return tuple(map(unichr, result))\n\nif __name__ == '__main__':\n    test_values = u'\ud56d\uac00\uc788\ub2ed\ub113\uc9e7'\n\n    for syllable in test_values:\n        print syllable, ':',\n        for s in decompose(syllable): print s,\n        print\n\nThis is the output in my console:\n\ud56d : \u1112 \u1161 \u11bc\n\uac00 : \u1100 \u1161\n\uc788 : \u110b \u1175 \u11bb\n\ub2ed : \u1103 \u1161 \u11b0\n\ub113 : \u1102 \u1165 \u11b2\n\uc9e7 : \u110d \u1161 \u11b2\n\n\n\n\n\n\n\n\n\nShare\n\n\nImprove this answer\n\n\n\n                        Follow\n                        \n\n\n\n\n\n\n\n\n\n\nedited Dec 27, 2011 at 1:26\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n            answered Dec 25, 2011 at 14:19\n\n\n\n\n\n\nJong Bor LeeJong Bor Lee\n\n3,84511 gold badge2727 silver badges2727 bronze badges\n\n\n\n\n\n\n\n2"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0e37cac8-a41c-4577-af00-51b525feee76
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 7065, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Jong Bor LeeJong Bor Lee\n\n3,84511 gold badge2727 silver badges2727 bronze badges\n\n\n\n\n\n\n\n2\n\n\n\n\n\n\n\n\n\n\nThanks Jong Bor, that works well. I changed the output in your example to return an encoded string. i.e.     if T == TBase:         u = unichr(L) + ' ' + unichr(V)     else:         u = unichr(L) + ' ' + unichr(V)  +  ' ' + unichr(T)     u.encode('utf-8')     return(u)          if name == 'main':     print (u'\ud56d'), ' --> ', decompose(u'\ud56d')     print (u'\uac00'), ' --> ', decompose(u'\uac00')\n\n\u2013\u00a010ToedSloth\n\n\nCommented\nDec 26, 2011 at 12:04\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n@10ToedSloth Your change makes sense since decompose takes an encoded string as a parameter, so I modified my code accordingly. I'm glad that you took the time to review the answer for a question you made two years ago :)\n\n\u2013\u00a0Jong Bor Lee\n\n\nCommented\nDec 27, 2011 at 1:23\n\n\n\n\n\n\n\nAdd a comment\n\u00a0|\u00a0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n            1"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e20fb369-c0b7-4640-80a4-3ca82e0e6fca
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 7890, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Add a comment\n\u00a0|\u00a0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n            1\n        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nI think what you are looking for is a Byte Array\nDim aByte() as byte\naByte=\"\ud55c\uae00\"\nshould give you the two unicode values for each character in the string\n\n\n\n\n\n\n\n\nShare\n\n\nImprove this answer\n\n\n\n                        Follow\n                        \n\n\n\n\n\n\n\n\n\n\n            answered Nov 16, 2009 at 13:47\n\n\n\n\n\n\nCharles WilliamsCharles Williams\n\n23.5k55 gold badges3939 silver badges3939 bronze badges\n\n\n\n\n\n\n\n1\n\n\n\n\n\n\n\n\n\n\nThanks, It works, I was confused by the byte ordering at first. Doh!\n\n\u2013\u00a010ToedSloth\n\n\nCommented\nNov 16, 2009 at 16:38\n\n\n\n\n\n\n\nAdd a comment\n\u00a0|\u00a0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n            0"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 9a9e57f9-f38c-4e09-badb-c1da247d6c97
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 8504, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Add a comment\n\u00a0|\u00a0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n            0\n        \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nI assume you got what you needed, but it seems rather convoluted. I don't know anything about this, but recently did some investigating of handling Unicode, and looked into all the string Byte functions, such as LeftB(), RightB(), InputB(), InStrB(), LenB(), AscB(), ChrB() and MidB(), and there's also StrConv(), which has a vbUnicode argument. These are all functions that I'd think would be used in any double-byte context, but then, I don't work in that environment so might be missing something very important.\n\n\n\n\n\n\n\n\nShare\n\n\nImprove this answer\n\n\n\n                        Follow\n                        \n\n\n\n\n\n\n\n\n\n\n            answered Nov 17, 2009 at 3:28\n\n\n\n\n\n\nDavid-W-FentonDavid-W-Fenton\n\n23.1k44 gold badges4747 silver badges5858 bronze badges\n\n\n\n\n\n\n\n1"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: ee2aaeb0-fa63-492c-8c0d-39dc6f84962d
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 9281, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "23.1k44 gold badges4747 silver badges5858 bronze badges\n\n\n\n\n\n\n\n1\n\n\n\n\n\n\n\n\n\n\nYeah the manual syllable decomposition does seem convoluted, I really expect there is a more standardised method. Charles's suggestion of just dropping it into a byte array works fine. But as you mentioned the same could be achieved using functions like AscB(), etc .\n\n\u2013\u00a010ToedSloth\n\n\nCommented\nNov 17, 2009 at 5:26\n\n\n\n\n\n\n\nAdd a comment\n\u00a0|\u00a0\n\n\n\n\n\n\n\n\n\n\n\n                                    Your Answer\n                                \n\n\n\n\n\n\n\n\nReminder: Answers generated by artificial intelligence tools are not allowed on Stack Overflow. Learn more\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThanks for contributing an answer to Stack Overflow!Please be sure to answer the question. Provide details and share your research!But avoid \u2026Asking for help, clarification, or responding to other answers.Making statements based on opinion; back them up with references or personal experience.To learn more, see our tips on writing great answers."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 084c8f3f-6a0f-431f-8be8-cf8466d23f7c
encoding: FLOAT32
metadata:
{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 10271, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Draft saved\nDraft discarded\n\n\n\n\n\n\n\n\n\n\n\n\nSign up or log in\n\n\n Sign up using Google\n                        \n\n Sign up using Email and Password\n                        \n\n\n\nSubmit\n\nPost as a guest\n\n\nName\n\n\n\n\n\n\n\n\n\nEmail\nRequired, but never shown\n\n\n\n\n\n\n\n\n\n\n\n\nPost as a guest\n\n\nName\n\n\n\n\n\n\n\n\n\nEmail\nRequired, but never shown\n\n\n\n\n\n\n\n\n\n\n\n                                            Post Your Answer\n                                        \n\n                                            Discard\n                                        \n\n                                                By clicking \u201cPost Your Answer\u201d, you agree to our terms of service and acknowledge you have read our privacy policy.\n\n\n\n\n\n\nNot the answer you're looking for? Browse other questions tagged excelvbams-accessunicodeunicode-normalization or ask your own question.                                \n\n\n\n\n\n\n\n                            The Overflow Blog\n                        \n\n\n \n\nAI agents that help doctors get paid"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e8d38c2a-ad02-4823-a802-c321afcfc801
encoding: FLOAT32
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{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 11219, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "AI agents that help doctors get paid\n\n\n\n\n \n\nLegal advice from an AI is illegal\n\n\n\n                            Featured on Meta\n                        \n\n\n \n\nThe December 2024 Community Asks Sprint has been moved to March 2025 (and...\n\n\n\n\n \n\nStack Overflow Jobs is expanding to more countries\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVisit chat\n\n\n\n\n\nLinked\n\n\n\n0\n\nGetting first symbol from a glyph\n\n\n\n\nRelated\n\n\n\n1\n\nConvert Korean Text to Unicode\n\n\n\n0\n\nExtracting unicode text from Excel spreadsheet using VBA\n\n\n\n0\n\nCopying Korean characters from Excel to TextEdit with AppleScript\n\n\n\n3\n\nRead Chinese characters from Excel worksheet? (Always returns \"????\")\n\n\n\n1\n\nStrip Chinese Characters from a string (vba)\n\n\n\n0\n\nVBA not recognizing Japanese characters\n\n\n\n2\n\nExtract text from a text file with Chinese characters using vba\n\n\n\n7\n\nRemove Unicode characters in a String\n\n\n\n5\n\nBreaking down a Hangul syllable into letters (jamo)\n\n\n\n1\n\nJava string text normalizer dublicate korean character"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 12121, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "1\n\nJava string text normalizer dublicate korean character\n\n\n\n\n\n\n\n            Hot Network Questions\n        \n\n\n\n\n                    How to determine what is opening tmp files when I invoke a subshell with ksh\n                \n\n\n\n                    What is the gameplay benefit of engaging with portal storms?\n                \n\n\n\n                    Are probabilities in games of chance any more objective than in other contexts?\n                \n\n\n\n                    Derashos Chasam Sofer on \u05d4\u05d2\u05d1\u05dc\u05ea of Har Sinai\n                \n\n\n\n                    Can the gravitational force of a black hole \"lock\" a particle in an unstable equilibrium?\n                \n\n\n\n                    Notepad++ find and replace string\n                \n\n\n\n                    SelectFirst and Hold\n                \n\n\n\n                    What is the difference between OBJECT of a preposition and COMPLEMENT of a preposition in English grammar"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 13086, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Which lubricant for plastic rail guides on sliding doors?\n                \n\n\n\n                    Place 5 dominoes so that horizontal and vertical sums are equal\n                \n\n\n\n                    More robust MOSFET gate: Which parameter to watch?\n                \n\n\n\n                    Is it OK to use longjmp to break out of qsort?\n                \n\n\n\n                    The significance of \"disciples AND Peter\" in Mark 16:7\n                \n\n\n\n                    Can a CLA allow selling exceptions without allowing relicensing to no longer be FOSS?\n                \n\n\n\n                    Where is my mistake in this limit problem?\n                \n\n\n\n                    Why did the Civil Service in the UK become so associated with Oxbridge?\n                \n\n\n\n                    Print the largest hidden double\n                \n\n\n\n                    Why would David not drink the water?"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 88a8f0eb-3cd6-4fc4-9f15-bb72295d3776
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{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 13954, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Why would David not drink the water?\n                \n\n\n\n                    How do I get rid of the yellow tint when using Sky Texture?\n                \n\n\n\n                    British TV show about a widowed football journalist\n                \n\n\n\n                    RAISERROR / THROW and sp_start_job Termination Behaviour\n                \n\n\n\n                    What would it take for an AI to have beliefs?\n                \n\n\n\n                    Does a boxplot assume interval data?\n                \n\n\n\n                    On the continuity a function given by evaluating compact subsets of smooth functions\n                \n\n\n\n            more hot questions\n        \n\n\n\n\n            Question feed\n        \n\n\n\n\n                Subscribe to RSS\n            \n\n\n\n                        Question feed\n                        To subscribe to this RSS feed, copy and paste this URL into your RSS reader.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nlang-vb\n\n\n\n\n\n\n\n\n\n\n\n\nStack Overflow\n\nQuestions\nHelp\nChat"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: fda0fea6-8be5-4e21-958c-7d28e2ac9e00
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{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 14874, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "lang-vb\n\n\n\n\n\n\n\n\n\n\n\n\nStack Overflow\n\nQuestions\nHelp\nChat\n\n\n\nProducts\n\nTeams\nAdvertising\nTalent\n\n\n\nCompany\n\nAbout\nPress\nWork Here\nLegal\nPrivacy Policy\nTerms of Service\nContact Us\nCookie Settings\nCookie Policy\n\n\n\n\nStack Exchange Network\n\n\n\n                                    Technology\n                                \n\n\n\n                                    Culture & recreation\n                                \n\n\n\n                                    Life & arts\n                                \n\n\n\n                                    Science\n                                \n\n\n\n                                    Professional\n                                \n\n\n\n                                    Business\n                                \n\n\n\n                                    API\n                                \n\n\n\n                                    Data\n                                \n\n\n\n\n\n\n\nBlog\nFacebook\nTwitter\nLinkedIn\nInstagram"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 5fa2f5e6-1150-42bb-896b-488df777dfc4
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{"source": "https://stackoverflow.com/questions/1741221/how-to-extract-characters-from-a-korean-string-in-vba", "title": "excel - how to extract characters from a Korean string in VBA - Stack Overflow", "language": "en", "start_index": 15774, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Blog\nFacebook\nTwitter\nLinkedIn\nInstagram\n\n\nSite design / logo \u00a9 2024 Stack Exchange Inc; \nuser contributions licensed under \nCC BY-SA\n. \nrev\u00a02024.12.17.20550"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 3af16f5c-7fea-41b6-81cd-e21b224a5208
encoding: FLOAT32
metadata:
{"source": "http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database", "title": "KAIST Scene Text Database - TC11", "language": "en", "start_index": 4, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "KAIST Scene Text Database - TC11\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\nNavigation menu\n\n\n\n\nToggle navigation\n\n\n\n\nTC11\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nKAIST Scene Text Database\n\n\nFrom TC11\n\n\n\t\t\t\t\tJump to:\t\t\t\t\tnavigation, \t\t\t\t\tsearch\n\nDatasets -> Datasets List -> Current Page\n\n\n\n\n\n\nCreated: 2011-01-11\n\n\nLast updated: 2012-10-17\n\n\nContents\n\n1 Contact Author\n2 License\n3 Current Version\n4 Keywords\n5 Description\n6 Related Ground Truth Data\n7 Related Tasks\n8 References\n9 Submitted Files\n\n9.1 Version 1.0\n\n\n\n\nContact Author\nProf. Jin Hyung Kim\nArtificial Intelligence and Pattern Recognition Lab,\nComputer Science Department of KAIST, KOREA\nTel: 82-42-350-3517\nEmail: Jkim @ kaist.ac.kr\n\nSeonghun Lee\nArtificial Intelligence and Pattern Recognition Lab,\nComputer Science Department of KAIST, KOREA\nEmail: leesh @ ai.kaist.ac.kr\n\nLicense\n\nThis work is licensed under a Creative Commons Attribution-ShareAlike License\n\nCurrent Version\n Example of images and ground truth information in the KAIST dataset.\n1.0"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 403f5595-0a96-4410-8c8b-b7870ddd4cd3
encoding: FLOAT32
metadata:
{"source": "http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database", "title": "KAIST Scene Text Database - TC11", "language": "en", "start_index": 889, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Current Version\n Example of images and ground truth information in the KAIST dataset.\n1.0\n\nKeywords\nScene Text, Korean, English, Signboard, Mobile phone image, Indoor image, Outdoor image\n\nDescription\nThe KAIST scene text dataset comprises 3000 images captured in different environments, including outdoors and indoors scenes under different lighting conditions (clear day, night, strong artificial lights, etc). Images were captured either by the use of a high-resolution digital camera or a low-resolution mobile phone camera. All images have been resized to 640x480.\nThe KAIST scene text database is categorized according to the language of the scene text captured: Korean, English (Number), and Mixed (Korean + English + Number). The scene text in the images is representative of common text in Korean streets or shops."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: a92c172e-e1b3-49c3-bc77-acab472df9f4
encoding: FLOAT32
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{"source": "http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database", "title": "KAIST Scene Text Database - TC11", "language": "en", "start_index": 1714, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Related Ground Truth Data\nKAIST Scene Text Ground Truth (text location, segmantation and recognition)\nRelated Tasks\nScene Text Localisation in the KAIST Dataset\nScene Text Segmentation in the KAIST Dataset\nReferences\nJehyun Jung, SeongHun Lee, Min Su Cho, and Jin Hyung Kim, \u201cTouch TT: Scene Text Extractor Using Touch Screen Interface\u201c, ETRI Journal 2011\nSeongHun Lee, Min Su Cho, Kyomin Jung, and Jin Hyung Kim, \"Scene Text Extraction with Edge Constraint and Text Collinearity Link,\" 20th International Conference on Pattern Recognition (ICPR), August 2010, Istanbul, Turkey.\nSubmitted Files\nVersion 1.0\nComplete Download (the directory structure of the zip file reflects the structure below) (364 MB)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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encoding: FLOAT32
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{"source": "http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database", "title": "KAIST Scene Text Database - TC11", "language": "en", "start_index": 2420, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Korean Language\nDigital Camera\nSignboard\nShadow (6.87 MB)\nLight (1.14 MB)\nOutdoor 1 (21.51 MB), Outdoor 2 (19.79 MB), Outdoor 3 (13.13 MB), Outdoor 4 (13.00 MB), Outdoor 5 (12.86 MB), Outdoor 6 (12.84 MB), Outdoor 7 (13.71 MB), Outdoor 8 (4.52 MB)\nIndoor 1 (14.14 MB), Indoor 2 (9.52 MB)\nNight (6.70 MB)\nBook Cover (6.18 MB)\nOthers (7.93 MB)\nMobile Phone\nSignboard\nShadow (645.54 KB)\nLight (336.4 KB)\nOutdoor (2.43 MB)\nIndoor (1.40 MB)\nBook Cover (18.63 MB)\nOthers (430.55 KB)\nEnglish Language\nDigital Camera\nSignboard\nShadow (2.16 MB)\nLight (2.33 MB)\nOutdoor 1 (8.44 MB), Outdoor 2 (10.53 MB), Outdoor 3 (2.46 MB)\nIndoor (8.44 MB)\nNight (2.32 MB)\nOthers (6.17 MB)\nMobile Phone\nSignboard\nOutdoor (828.2 KB)\nIndoor (2.36 MB)\nLight (2.22 MB)\nMixed Language Content\nDigital Camera\nShadow (2.18 MB)\nLight (460.19 KB)\nOutdoor 1 (24.34 MB), Outdoor 2 (12.01 MB), Outdoor 3 (11.69 MB), Outdoor 4 (11.90 MB), Outdoor 5 (11.73 MB), Outdoor 6 (7.87 MB)\nIndoor 1 (10.94 MB), Indoor 2 (1.44 MB)\nNight (4.45 MB)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database", "title": "KAIST Scene Text Database - TC11", "language": "en", "start_index": 3363, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Indoor 1 (10.94 MB), Indoor 2 (1.44 MB)\nNight (4.45 MB)\nOthers (5.13 MB)\nMobile Phone\nSignboard\nOutdoor (2.42 MB)\nIndoor (1.72 MB)\nBook Cover (3.21 MB)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 28857e5e-a4c3-4e4b-b6bc-881aac85a8a0
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{"source": "http://www.iapr-tc11.org/mediawiki/index.php/KAIST_Scene_Text_Database", "title": "KAIST Scene Text Database - TC11", "language": "en", "start_index": 3516, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "This page is editable only by TC11 Officers .\n\n\n\n\n\n \n\t\t\t\tRetrieved from \"http://www.iapr-tc11.org/mediawiki/index.php?title=KAIST_Scene_Text_Database&oldid=1773\"\t\t\t\t\n \n\n\n\n\n\n\n\n\n\nPrivacy policyAbout TC11DisclaimersLogin / Create AccountPowered by MediaWiki"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e6999b51-4cd4-4d85-8771-b231f6c15c08
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{"source": "https://www.reddit.com/r/galaxys10/comments/cjpim3/smart_select_extract_other_languages/", "title": "Reddit - Dive into anything", "language": "en-US", "start_index": 4, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Reddit - Dive into anything\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOpen menu\n\nOpen navigation\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nGo to Reddit Home\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nr/galaxys10\n                \n\nA chip\n\n\n\n\n\n\n\n\nA close button\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGet App\n\n\n\n\n\n        Get the Reddit app\n      \n\n\n\n\n\n\n\n\nLog In\n\nLog in to Reddit\n\n\n\n\n\n\n\n\n\nExpand user menu\nOpen settings menu\n\n\n\n\n\n\n\n\n\n\n\nLog In / Sign Up\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdvertise on Reddit\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nShop Collectible Avatars\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n              Go to galaxys10\n            \n\n\n\n\n\n\n\n\n\nr/galaxys10\n\n\n\n\n\n\n\n\n\n\n\nr/galaxys10\n\n\n\n\n\n\n\n              Samsung Galaxy S10\n            \n\n\n\n\n\n\nMembers\n\n\n\n\n\n\n\nOnline\n\n\n\n\n\n\n\n\n\n\u2022\n\n\n\n\n\n\nStaygoldponiboy\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n      ADMIN\n    \n\n      MOD\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n       Smart Select extract other languages"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 4f61682a-1de5-4a5c-af28-e18d6bebed01
encoding: FLOAT32
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{"source": "https://www.reddit.com/r/galaxys10/comments/cjpim3/smart_select_extract_other_languages/", "title": "Reddit - Dive into anything", "language": "en-US", "start_index": 847, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "MOD\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n       Smart Select extract other languages \n\n\n\n\n\n\n\n    I used to be able to extract text other than English (Korean), but now extracting text in other languages just extracts and changes the text into english alphabet. To be clear, it doesn't translate, just copies gibberish. Also, I have both englsih and Korean as keyboards.\n  \n    Did something change or is there a setting for this somewhere?\n  \n\n\n\n      Read more\n       \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n              Top 2%\n            \n\n              Rank by size \n\n\n\n\n\n\n\n\n\n Public\n   Anyone can view, post, and comment to this community\n\n\n\n\n\n\n\n\n        Top Posts\n      \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Reddit \n\nreReddit: Top posts of July 30, 2019\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Reddit \n\nreReddit: Top posts of July 2019\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n Reddit \n\nreReddit: Top posts of 2019"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 2f35a9c7-80c3-4a6b-a34e-a3756fabe859
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{"source": "https://www.reddit.com/r/galaxys10/comments/cjpim3/smart_select_extract_other_languages/", "title": "Reddit - Dive into anything", "language": "en-US", "start_index": 1803, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Reddit \n\nreReddit: Top posts of 2019\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n&nbsp;\n\n\n\n\n\n\n\n\n\n\n\n&nbsp;\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\nTOPICS\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInternet Culture (Viral)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAmazing\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnimals & Pets\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCringe & Facepalm\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFunny\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInteresting\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMemes\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOddly Satisfying\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nReddit Meta\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWholesome & Heartwarming\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGames\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAction Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdventure Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEsports\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaming Consoles & Gear\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaming News & Discussion\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMobile Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOther Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRole-Playing Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSimulation Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSports & Racing Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nStrategy Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTabletop Games\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nQ&As\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nQ&As\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nStories & Confessions"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 80dd743a-cc0d-4499-b45a-6a52c98bd1da
encoding: FLOAT32
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{"source": "https://www.reddit.com/r/galaxys10/comments/cjpim3/smart_select_extract_other_languages/", "title": "Reddit - Dive into anything", "language": "en-US", "start_index": 2640, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Q&As\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nQ&As\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nStories & Confessions\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTechnology\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n3D Printing\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nArtificial Intelligence & Machine Learning\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nComputers & Hardware\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nConsumer Electronics\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDIY Electronics\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProgramming\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSoftware & Apps\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nStreaming Services\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTech News & Discussion\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nVirtual & Augmented Reality\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPop Culture\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCelebrities\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCreators & Influencers\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGenerations & Nostalgia\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPodcasts\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nStreamers\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTarot & Astrology\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMovies & TV\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAction Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnimated Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nComedy Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrime, Mystery, & Thriller Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDocumentary Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDrama Movies & Series"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e7891579-8b87-4b82-a58f-7b7e1aefe976
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{"source": "https://www.reddit.com/r/galaxys10/comments/cjpim3/smart_select_extract_other_languages/", "title": "Reddit - Dive into anything", "language": "en-US", "start_index": 3552, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Documentary Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDrama Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFantasy Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHorror Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMovie News & Discussion\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nReality TV\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRomance Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSci-Fi Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSuperhero Movies & Series\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTV News & Discussion\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n  \n\n\n\n\n\n\n\n\nRESOURCES\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAbout Reddit\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nAdvertise\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nHelp\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nBlog\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nCareers\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nPress\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nCommunities\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nBest of Reddit\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nTopics\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nContent Policy\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nPrivacy Policy\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\nUser Agreement\n\n\n\n\n\n\n\n\n\n\n\n\n\n \nReddit, Inc. \u00a9 2024. All rights reserved."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 814a74c4-d47f-4e86-83e3-17f81baf1b53
encoding: FLOAT32
metadata:
{"source": "https://docsbot.ai/prompts/technical/ocr-and-extraction", "title": "OCR and Extraction - AI Prompt", "description": "Extract structured information from document images without translating Korean text. Free Technical prompt for ChatGPT, Gemini, and Claude.", "language": "en", "start_index": 0, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "OCR and Extraction - AI PromptNEWOptimize Your Chatbots with our Custom Prompt Generator and Prompt Library\u00a0\u2192DocsBotOpen main menuUse CasesFeaturesPricingDocsDevelopersBlogLog inTry FreeHomePromptsTechnicalOCR and ExtractionTechnical AI PromptOCR and ExtractionExtract structured information from document images without translating Korean text. Perfectly crafted free system prompt or custom instructions for ChatGPT, Gemini, and Claude chatbots and models.OCRInformation ExtractionMultilingualKoreanYou are a highly accurate OCR and information extraction tool. Your task is to extract all structured information from a provided document image. Please ensure that any Korean text found in the image remains untranslated and intact. Focus on accuracy and completeness so that no text is missed."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0c72ef6e-4278-4e81-9f3d-091ab1cd0926
encoding: FLOAT32
metadata:
{"source": "https://docsbot.ai/prompts/technical/ocr-and-extraction", "title": "OCR and Extraction - AI Prompt", "description": "Extract structured information from document images without translating Korean text. Free Technical prompt for ChatGPT, Gemini, and Claude.", "language": "en", "start_index": 797, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "# Steps\n1. Process the image to identify all text regions, paying close attention to both text and non-text elements that need labeling.\n2. Accurately extract the texts from the identified regions while maintaining the structural format.\n3. Preserve any Korean text in its native form, ensuring it remains untranslated.\n4. Compile a structured output that includes all the extracted information.\n\n# Output Format\nOutput a JSON object where each field corresponds to a specific section of the document. For example:\n\n```\n{\n  \"Title\": \"[Title Text]\",\n  \"Date\": \"[Date Text]\",\n  \"KoreanText\": \"[Korean Text]\",\n  \"Body\": \"[Body Text]\",\n  \"OtherSections\": [\n    {\n      \"SectionName\": \"[Section Name]\",\n      \"Text\": \"[Section Text]\"\n    }\n  ]\n}\n```"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 7b22edf1-c0b2-440d-967f-0b3a51b03ba3
encoding: FLOAT32
metadata:
{"source": "https://docsbot.ai/prompts/technical/ocr-and-extraction", "title": "OCR and Extraction - AI Prompt", "description": "Extract structured information from document images without translating Korean text. Free Technical prompt for ChatGPT, Gemini, and Claude.", "language": "en", "start_index": 1543, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "# Notes\n- Pay attention to text alignment and potential noise in the background.\n- Ensure that all text types (e.g., headers, footers) are captured.\n- The structured output should be in English where applicable, with Korean text only left untranslated.\n- This is intended for documents containing multiple languages, primarily English and Korean."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: c423574e-4da8-4d58-a796-a9a44229e230
encoding: FLOAT32
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{"source": "https://docsbot.ai/prompts/technical/ocr-and-extraction", "title": "OCR and Extraction - AI Prompt", "description": "Extract structured information from document images without translating Korean text. Free Technical prompt for ChatGPT, Gemini, and Claude.", "language": "en", "start_index": 1892, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "CopyCategory: TechnicalGenerate your own AI promptCreate your own custom GPT chatbot with your own data and knowledge. Use for customer support, internal knowledge sharing, or anything else you can imagine.Loved by 45k+ usersCreate Your Free Custom GPTMore Technical Prompts1-Minute Trading ScriptGenerates a 1-minute buy/sell trading script from given docs.10 Markup LanguagesLists 10 lightweight markup languages like Markdown, with unique features and use cases.2D Interactive Environment RoadmapRoadmap for creating a multiplayer 2D virtual office environment.3D Animation and STEP FileCreate a 3D animation from a sketch and produce a STEP file.3D Camera Interaction ExplanationExplains camera behavior on double-clicking in 3D.3D Developer PortfolioCreate a responsive 3D animated portfolio using React.3D Model Creation GuideGuide to creating a 3D model using modeling software.3D PCB ModelingGenerate a 3D model of a PCB with all specified components and connections.View all Technical"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: f5ee755d-e6ca-42d0-876f-69a66d1b6c99
encoding: FLOAT32
metadata:
{"source": "https://docsbot.ai/prompts/technical/ocr-and-extraction", "title": "OCR and Extraction - AI Prompt", "description": "Extract structured information from document images without translating Korean text. Free Technical prompt for ChatGPT, Gemini, and Claude.", "language": "en", "start_index": 2801, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "a 3D model of a PCB with all specified components and connections.View all Technical promptsBrowse all categoriesExplore More Free Prompt ToolsAI Prompt LibraryAI Prompt GeneratorChatGPT Prompt GeneratorClaude Prompt GeneratorImage to Prompt GeneratorSee all free tools \u2192FooterAn UglyRobot thing.X/TwitterLinkedInGitHubPagesUse CasesFeaturesPricingDocsDevelopersBlogMetaLegalPressAffiliate ProgramFree AI ToolsAI Text HumanizerAI Text ParaphraserYouTube Blog Post GeneratorYouTube SummarizerImage Description GeneratorImage Caption GeneratorAI Prompt LibraryAI Prompt GeneratorSupport Savings CalculatorWebsite FAQ GeneratorLLM API Price CalculatorAI Terms GlossaryAll Free Tools \u2192Other ProductsImajinn AIInfinite UploadsComparisonsChatbase AlternativeCustomGPT AlternativeAda AI AlternativeIntercom FIN AlternativeForethought AI AlternativeOpenAI GPTs AlternativeMy AskAI AlternativePDF.ai AlternativeFor IndustriesAgriculture & Forestry/WildlifeBusiness & InformationFinance & InsuranceFood &"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0ca4fe58-7fd2-4561-a537-7309f3aaa8da
encoding: FLOAT32
metadata:
{"source": "https://docsbot.ai/prompts/technical/ocr-and-extraction", "title": "OCR and Extraction - AI Prompt", "description": "Extract structured information from document images without translating Korean text. Free Technical prompt for ChatGPT, Gemini, and Claude.", "language": "en", "start_index": 3707, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "IndustriesAgriculture & Forestry/WildlifeBusiness & InformationFinance & InsuranceFood & HospitalityGamingHealth ServicesMotor VehicleNatural Resources/EnvironmentalPersonal ServicesReal Estate & HousingSafety/Security & LegalTransportationStay Updated with Latest FeaturesThe latest news and updates, sent to your inbox occasionally.Email addressSubscribe\u00a9 2024 UglyRobot, LLC. All rights reserved."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: d603a788-1c00-430f-9b86-40311f3f1170
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 10, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\n\n\nLog in\n\n\n\n\n\n\n\nMenu\n\n\n\n\n                            Find a journal\n                        \n\n                            Publish with us\n                        \n\n                            Track your research\n                        \n\n\n\n\n\n\nSearch\n\n\n\n\n\n\nCart\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHome\n\n\n\n\n\nApplied Intelligence\n\n\n\n\n\nArticle\n\n\n\nA large-scale dataset for korean document-level relation extraction from encyclopedia texts\n\n\nOpen access\n\n\n                            Published: 02 July 2024\n\n\n\n\nVolume\u00a054,\u00a0pages 8681\u20138701, (2024)\n                        \n\nCite this article\n\n\n\n\n\n\nDownload PDF"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0896f475-e845-49be-91bf-15c1ed12d4bd
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 888, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Cite this article\n\n\n\n\n\n\nDownload PDF\n\n\n\n\n\n\n                            You have full access to this open access article\n\n\n\n\n\n\n\n\nApplied Intelligence\n\n\n                                Aims and scope\n                                \n\n\n                                Submit manuscript\n                                \n\n\n\n\n\n\n\n\n\n\n                                A large-scale dataset for korean document-level relation extraction from encyclopedia texts\n                            \n\n\n\n\nDownload PDF\n\n\n\n\n\n\n\n\n\nSuhyune Son1\u00a0na1, Jungwoo Lim1\u00a0na1, Seonmin Koo1\u00a0na1, Jinsung Kim1\u00a0na1, Younghoon Kim2, Youngsik Lim2, Dongseok Hyun2 & \u2026Heuiseok Lim\u00a0\n            ORCID: orcid.org/0000-0002-9269-11571\u00a0Show authors\n\n\n\n\n\n803 Accesses\n\n\nExplore all metrics"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 6d6c8b49-de85-4d58-bd59-1b92d6cf4fe2
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 1596, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "803 Accesses\n\n\nExplore all metrics \n\n\n\n\n\n\n\n\n\n\nAbstractDocument-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on document-level RE in English, Korean document-level RE research is still at the very beginning due to the absence of a dataset. To accelerate the studies, we present  TREK  (Toward Document-Level Relation Extraction in Korean) dataset constructed from Korean encyclopedia documents written by the domain experts. We provide detailed statistical analyses for our large-scale dataset and human evaluation results suggest the assured quality of  TREK . Also, we introduce the document-level RE model that considers the named entity-type while considering the Korean language\u2019s properties. In the experiments, we demonstrate that our proposed model outperforms the baselines and conduct qualitative analysis.\n\n\n\nSimilar content being viewed by others"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e3f852a4-60ca-40a1-a86a-bde0216604be
encoding: FLOAT32
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 2509, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Similar content being viewed by others\n\n\n\n\n\n\nDocument-Level Relation Extraction in\u00a0Russian\n                                        \n\n\nChapter\n\u00a9 2024\n\n\n\n\n\n\n\n\n\nETCGN: entity type-constrained graph networks for document-level relation extraction\n                                        \n\n\nArticle\n20 August 2024\n\n\n\n\n\n\n\n\n\nEnhancing Document-Level Relation Extraction with\u00a0Entity Pronoun Resolution and\u00a0Relation Correlation\n                                        \n\n\nChapter\n\u00a9 2025\n\n\n\n\n\n\n\n\nExplore related subjects\nDiscover the latest articles, news and stories from top researchers in related subjects.\n\n\nArtificial Intelligence\n\n\n\n\n\n\nUse our pre-submission checklist\n\n\nAvoid common mistakes on your manuscript."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e887498d-8813-4017-9ed7-8ff3f8b70e12
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 3225, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "1 IntroductionRelation extraction (RE) is a task that captures relational facts between entities from plain text and serves as the basis of large-scale knowledge base construction and population research\u00a0[1,2,3]. Previous RE studies focus on sentence-level RE which is to extract relational facts from a sentence\u00a0[4,5,6], but the sentence-level RE models only concentrate on the local context within the single sentence\u00a0[7, 8].For the comprehensive context understanding capability of the RE model, one research\u00a0[9] builds document-level RE dataset in English, and it derives widespread research\u00a0[10,11,12,13]. However, in Korean, document-level RE research is hardly conducted due to the absence of a document-level RE dataset. Although the Korean cross-sentence-level RE dataset is collected [14], it is built on the paragraph-level so that the relations spread across the multiple paragraphs in a document are not considered. In other words, they constructed the dataset considering"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 47338329-b885-40c3-b989-439770323ed7
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 4120, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "in a document are not considered. In other words, they constructed the dataset considering inter-sentence relations and intra-sentence relations only within paragraphs. Therefore, a document-level RE dataset is required to accelerate the document-level RE research in Korean.Simply translating English document-level RE as an alternative to the absence of a dataset is inappropriate. The reason is that Korean is an agglutinative language, whereas English belongs to an inflectional language\u00a0[15]. As an agglutinative language, one of the characteristics of Korean language is the relatedness between lexical morpheme and functional morpheme\u00a0[16, 17]. As the complete meaning of the word in Korean depends on the type of functional morpheme, it is essential to identify the functional morpheme\u2019s role in the sentence. For example, in the sentence \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 12b52ff6-4e1e-4699-b56a-86a0523c5f12
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 4970, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": ". (An apple is at home.)\u201d, the word \u201c\n\n\u201d is a combination of the entity \u201c\n\n(apple)\u201d and functional morpheme \u201c\n\n[-neun]Footnote 1\u201d. \u201c\n\n\u201d serves the subject role because \u201c\n\n[-neun]\u201d is a subjective functional morpheme. Meanwhile, in \u201c\n\n. (I like the apple at home.)\u201d, \u201c\n\n\u201d indicates the object as an objective functional morpheme \u201c\n\n[-reul]\u201d is combined. Likewise, simply translating the English dataset is not desirable for collecting Korean dataset. Therefore, building Korean-based RE resources from scratch regarding the property is crucial.Fig. 1An example from  TREK  dataset. For clarity, named entity involved in relations are colored in blue or green and other named entities are underlined. The relational triple (\n\n, org:founded, \n\n) is an inter-sentence relation extracted from multiple sentences and (\n\n, org:members,"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 526efe02-ca98-41a6-9b8a-bb67f2e191c5
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 5801, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": ") is an intra-sentence relation extracted from a single sentenceFull size imageIn this paper, we present a  TREK  (Toward document-level Relation Extraction in Korean) dataset from Korean encyclopedia documents written by the domain experts. We construct the dataset with distant-supervision manner and conduct an additional human inspection to consider the missing or noisy annotations. As shown in Fig. 1,  TREK  dataset consists of a document and relational triples, i.e., (head, relation, tail). Along with the dataset construction, we analyze  TREK  dataset regarding named entity-types and relation classes. Furthermore, the human evaluation results suggest that our document RE dataset is quality assured. We also propose a Korean document-level RE model that employs named entity-type information and reflects the characteristics of the Korean language. Since the role of the word changes according to the type of functional morpheme, we adopt special tokens surrounding the entity to let"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: dd57d7a5-a4f1-4153-90ac-f89ff6bee68d
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 6698, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "according to the type of functional morpheme, we adopt special tokens surrounding the entity to let the model distinguish the entity from the functional morpheme. We show the improved performance of our model on  TREK  and conduct qualitative analysis as well.Our contributions are as follows:"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e5e53250-abba-464d-9f55-d454473e7160
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 6993, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "We introduce a large-scale Korean document-level RE dataset,  TREK  (Toward document-level Relation Extrac-tion in Korean), based on an encyclopedia with distant-supervision and human inspection.  TREK  dataset contains a total of 285,696 examples with 15,703 documents, 329,099 sentences, and 100,776 entities. Our human evaluation also shows the ensured quality of our dataset.\n\n\nWe propose the document-level RE model that considers named entity-type information within the documents while taking into account of Korean language\u2019s characteristics.\n\n\nWe show our model\u2019s effectiveness on  TREK  dataset in the experiments and demonstrate its enhanced understanding capability through qualitative analysisFootnote 2"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e665b128-7e92-4fc5-9c8a-644ed2cb9ad5
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 7711, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "2 Related work2.1 Document-level Relation ExtractionDocument-level RE is challenging since entities are spread within multiple sentences\u00a0[9]. To solve this problem, diverse studies have been conducted regarding mentions and entities. The method that uses the selective attention network, which regards the same entities with different positions as distinct mention representations to capture the relations between the scattered entities, is proposed\u00a0[18]. Also, a study\u00a0[19] considers various kinds of mention dependencies exhibited in documents, while another study [20] introduces mention-level RE model to extract the key mention pairs in the document. The method that utilizes relation information is also proposed. A study\u00a0[21] uses relation embedding to capture the co-occurrence correlation of relations.On the other hand, the research that focuses on an entity also appears. One research mitigates the sparsity of the entity-pair representation and achieves a holistic understanding of the"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 7bb2e4da-7874-4e6c-985b-9d73118c0a39
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 8617, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "the sparsity of the entity-pair representation and achieves a holistic understanding of the document[22]. In addition, the method that enriches the entity-pair representation by introducing an axial attention mechanism with adaptive focal loss is proposed [23]. By exploiting the entity-types that provide additional information on the relatedness between each entity pair, various research shows improved performance on document-level RE task\u00a0[24]. Likewise, diverse branches of research are widely conducted in document-level RE in English.Fig. 2The process of  TREK  dataset construction. For comprehension, we provide an example in English. The original example in Korean is described in Appendix B. In phase 1 and 2, the entity \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: eb06039e-cb7b-447b-a51b-beac563e9117
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 9353, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(the Holy See)\u201d marked in blue indicates the subject inserted by pseudo-subject insertion. The highlighted texts and solid green lines are the named entities and relations, respectively. In phase 3, the dashed gray indicates deleted relation and the red lines indicate re-assigned relationsFull size image2.2 Relation extraction in koreanIn previous RE research in Korean, sentence-level RE studies have been conducted actively\u00a0[25]. There exists research that considers Korean characteristics by exploiting dependency structures to find proper relation in a sentence\u00a0[25,26,27]. Moreover, entity position tokens are utilized to capture contextual information in a sentence\u00a0[28]. These studies are promoted by the publicly available datasets to build sentence-level RE models. A sentence-level RE dataset, KLUE\u00a0[29], is published to overcome the lack of up-to-date Korean resources. Additionally, a Korean cultural heritage corpus\u00a0[30] for entity-related tasks is disclosed. Even though the Korean"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 3520002b-07b2-444f-bf7f-49978de5b934
encoding: FLOAT32
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 10252, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Korean cultural heritage corpus\u00a0[30] for entity-related tasks is disclosed. Even though the Korean cross-sentence-level RE dataset, Crowdsourcing \u00a0[14], is presented, this dataset only considers the paragraph-level, not the document-level. In other words, the relations spread across the multiple paragraphs in a document do not exist, which limits the capability of document-level REFootnote 3. Another research\u00a0[31] presents HistRED constructed from Yeonhaengnok, which is a collection of records written in Korean and Hanja, classical Chinese writing. Due to the limited scope of HistRED to specific domains, history, its applicability to general domains is constrained. Furthermore, all existing datasets rely on human annotation, making it challenging and costly to construct large-scale data. Therefore, document-level RE resources are limited, and this leads to a lack of studies on document-level RE in Korean.3 TREK datasetWe first describe how we build our  TREK  (Toward document-level"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: cd113814-940c-4939-b771-375a2b52697e
encoding: FLOAT32
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 11157, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "RE in Korean.3 TREK datasetWe first describe how we build our  TREK  (Toward document-level Relation Extraction in Korean) dataset in Section\u00a03.1. In Section\u00a03.2, we analyze the  TREK  dataset and describe the statistics on the types of named entities and relation classes. We conduct human evaluation on our dataset in Section\u00a03.3.3.1 Data constructionWe first annotate both entities and relations in a distant supervision manner from NAVER encyclopedia documentsFootnote 4, which consists of refined documents on diverse topics. Then, we additionally conduct a human inspection to consider missing or noisy annotations. To build a document-level RE dataset, we follow four steps; 1) named entity annotation, 2) sentence-level relation annotation, 3) document-level relation construction, and 4) human inspection.Named entity annotation (NEA) We annotate the named entity to the documents as illustrated in the first phase of Fig. 2. Owing to the characteristic of the NAVER encyclopedia, most of"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: f620c5df-0485-4393-bdf9-414e48f4976c
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 12062, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "in the first phase of Fig. 2. Owing to the characteristic of the NAVER encyclopedia, most of the sentences in the raw documents do not have a subject which is a document\u2019s titleFootnote 5Thus, we manually insert the subject and annotate the named entities in the document. In detail, we first find the sentences where the subject is missing by utilizing a pre-trained dependency parser library\u00a0[32] and then insert the document\u2019s title as the subject. Also, we concatenate the subjective functional morpheme at the end of the title-entity to indicate the subject in the sentence. This pseudo-subject insertion process clarifies the structure of the sentence, and the named entity recognition (NER) model predicts the position and type of each entity from the clarified sentence.Since Korean belongs to an agglutinative language, distinguishing entity and functional morpheme is critical in NER. Therefore, we let the NER model annotate being aware of the functional morpheme.In addition to defined"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 21e41624-4bc6-421f-b069-de21f0e4aee1
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 12968, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "we let the NER model annotate being aware of the functional morpheme.In addition to defined entity types, we define the named entity-type \u2018TITLE\u2019 and assign it to the entities which are equal to the title due to its importance in the document. We use pre-trained model which is trained with Korean documentsFootnote 6 based on the ELECTRA\u00a0[33] architecture and fine-tune the pre-trained model with KLUE-NER \u00a0[29] datasetFootnote 7.Sentence-level Relation Annotation (SRA) After annotating the named entity in the documents, we assign the relation between the two entities in a sentence.We implement the sentence-level RE models by considering the characteristics of the Korean language. Since Korean is an agglutinative language that forms a word with lexical morpheme and functional morpheme, capturing the scope of the entity in the sentence is significant\u00a0[15].Table 1 The performance of sentence-level RE models according to the type of entity position token(s)Full size tableTherefore, we"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 1598aa3e-3382-4312-b9ad-bb0c1f8ac14f
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 13874, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "RE models according to the type of entity position token(s)Full size tableTherefore, we employ entity position tokens to denote the position of subject and object entities in a sentence following previous Korean research\u00a0[28]. Start position tokens are inserted at the beginning of subject and object entities, while end position tokens are appended at the end of subject and object entities. We conduct experiments on sentence-level RE performance utilizing different types of representations as presented in Table 1.Table 2 The comparison between Korean RE datasetsFull size tableFrom the result, it is shown that the KLUE-RoBERTa utilizing [END] token embeddings shows the best performance among the others. It implies that distinguishing the borderline between the entity and functional morpheme is critical for predicting the relation. Consequently, we annotate intra-relation utilizing the KLUE-RoBERTa with [END] token, which shows the best performance.We train the sentence-level RE model"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: f2ae880b-46a1-4872-9a09-7e0e5df30601
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 14783, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "with [END] token, which shows the best performance.We train the sentence-level RE model with KLUE-RoBERTa based on KLUE-RE \u00a0[29] datasetFootnote 8.Document-level Relation Construction (DRC) For inter-sentence relations, we remove the pseudo-inserted subject, re-assigning the relation of the removed entity to the existing subject in the documents. For example, as depicted in phase 3 of Fig. 2, the red line indicates the re-assigned relation. When \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 191e8b9b-7cd5-4bfc-82c3-7ba9e31472b5
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 15236, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(the Holy See)\u201d inserted by the pseudo-subject insertion in sentence [2] is removed, the relation \u201cr2: org:member_of\u201d is re-assigned to \u201c\n\n(the Holy See)\u201d of sentence [1] and \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 34171eab-f703-420a-b3df-220b3748a335
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 15414, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(Italy)\u201d from sentence [2].Human Inspection Our human inspection process is divided into annotation and filtering to handle missing and noisy annotations. First, when assigning inter-sentence relations based on subjects in phase 3, the relation between non-subject entities may exist in the document. To consider these relations, we additionally annotate all possible relations in the document with humans and collect missing relations between non-subject entities. As a result, 63,482 examples are additionally annotated.Afterward, we exclude incorrect examples with the human-filtering process to remove the noises from the automatically annotated examples following previous research \u00a0[34]. In detail, human workers are instructed to decide whether the given example is valid or invalid. We delete the examples that are determined as invalid, and 14,663 examples are removed. The details of annotation and filtering are described in Appendix \u00a0C.Table 3 Statistics of  TREK  dataset split. Dev."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 5f444b68-73b0-439c-8efd-2715d5715dc2
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 16319, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "and filtering are described in Appendix \u00a0C.Table 3 Statistics of  TREK  dataset split. Dev. indicates the development setFull size table3.2 Data statisticsWe compare  TREK  dataset with existing Korean RE datasets\u00a0[14, 29, 30] as in Table 2.TREK  dataset has approximately three times as many examples at a minimum compared to the other datasets. Our dataset covers both intra-sentence and inter-sentence relations at a document-level, and the ratios of intra-relation and inter-relation are 25.31% and 74.69%, respectively. This ratio is reasonable for a document-level RE dataset, considering DocRED\u00a0[9], which has 18.49% and 81.51% of intra and inter-relation. Our dataset encompasses a general domain and is rich in diverse aspects, including the number of documents, examples, sentences, and entities compared to the existing datasets. Table 3 shows the statistics of train, development, and test sets in our  TREK  dataset. The dataset contains a total of 285,696 examples with 15,703"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 1801134b-c3e1-42b5-bda4-c43fad61a9f3
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 17212, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "and test sets in our  TREK  dataset. The dataset contains a total of 285,696 examples with 15,703 documents, 329,099 sentences, and 100,776 entities. The samples of  TREK  dataset are also described in Appendix D.Types of Named Entity Figure\u00a03 depicts the distribution of the named entity in  TREK  dataset. The distributions of each dataset split are depicted in Appendix E.Out dataset has 11 named entity-types including LOCATION (30.65 %), CIVILIZATION (17.18 %), PERSON (13.44 %), TITLE (12.90 %), etc. The descriptions of each entity-type are indicated in Appendix F.Fig. 3Types of named entity distribution of  TREK  datasetFull size imageFig. 4Relation class distribution of  TREK  datasetFull size imageFig. 5The overall architecture of the TREKER. TREKER reconstructs a given input document and performs three subtasks, i.e., coreference resolution, named entity prediction, and relation extraction in a multi-tasking manner. The reconstructed document D\u2019 is fed in to the pre-trained"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: fe75c927-4f42-4ee3-b36f-cd67bbc933ae
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 18109, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "extraction in a multi-tasking manner. The reconstructed document D\u2019 is fed in to the pre-trained language model (PLM) and we obtain the document embedding H. m is the mention embeddings from H. Also, v is the entity mention embedding for NEP while V is the entity embedding for bilinear operation in RE prediction. \\(\\mathcal {L}\\) indicates the lossFull size imageRelation Classes Figure 4 illustrates the distribution of relation classes in  TREK  dataset. The relation class distributions of the dataset splits are depicted in Appendix E. Our dataset consists of 26 various relation types, including org:member_of (31.40 %), org:founded_by (16.32 %), per:colleagues (9.12 %), org:place_of_headquarters (7.69 %), etc. The descriptions of each relation class are shown in Appendix G. We also consider the inverse relation type such as \u2018org:members\u2019 and \u2018org:member_of\u2019. For example, when the head is a higher organization, and the tail is affiliated with the head, the relation is tagged as"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 3bc99a62-d01f-446d-a17e-f4cafef985d9
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 19002, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "head is a higher organization, and the tail is affiliated with the head, the relation is tagged as \u2018org:members\u2019. On the other hand, \u2018org:member_of\u2019 means that the head belongs to the higher-level organization, which is the tail. Therefore, the relation classes cover diverse relations between entities, including inverse relations.3.3 Human evaluationWe conduct a human evaluation on our dataset,  TREK . We collect 29 workers who are Korean undergraduate students. We randomly chose 485 documents from the dataset. We asked the workers to evaluate whether the given head and tail are mapped to the given relation appropriately based on the document. The score is scaled from 1 to 3, and a higher score indicates that the relation is more appropriately annotated. As a result, 40.73% of the examples are evaluated as appropriate and 35.26% as plausible. Only 24.00% of the examples are regarded as less appropriate relations. The agreement between the annotators is calculated with Fleiss\u2019 Kappa"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: d04c41fc-9aa0-4143-98d0-8d540b799e78
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 19901, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "less appropriate relations. The agreement between the annotators is calculated with Fleiss\u2019 Kappa coefficient\u00a0[35] and is 0.4763, implying moderate agreement. In this result, we demonstrate the assured quality of  TREK  dataset.4 ModelWe implement our Korean document-level RE model based on  TREK  dataset considering named entity-type information and Korean language characteristics, and entitled as TREKER. As illustrated in Fig. 5, our model aims to predict the relation when the document and two entities, head and tail, are given. We first reconstruct a document input to reflect the properties of the Korean language. Then, the model extracts embeddings of the entity mention which represent the same entity scattered in the documents. With the extracted entity mention embeddings, the model trains on coreference resolution, named entity prediction, and relation extraction tasks in a multi-tasking manner.4.1 Input document reconstructionIn  TREK  dataset, a document D has a set of"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 745d1d25-ab65-4684-a13c-c0604a5c8d65
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 20794, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "multi-tasking manner.4.1 Input document reconstructionIn  TREK  dataset, a document D has a set of entities \\(E = \\left\\{ e_1, e_2, \\cdots , e_{|E|}\\right\\} \\). With the given documents and entities, we reconstruct the input document. We mark the special token with an asterisk (*) at the start and the end of every entity mention allowing the model to recognize and comprehend the functional morphemes and entity. Subsequently, the [CLS] token is inserted at the start of the document and the [SEP] token is placed at the end of every sentence. The reconstructed document D\u2019 is then fed into the pre-trained language model and we obtain document embedding H \\(\\in \\) \\(\\mathrm {\\mathbb {R}}^{T \\times dim}\\) . T and dim indicate the maximum length of tokens in D\u2019 and the dimension of the embeddings, respectively. From the document embedding H, we define the mention embedding \\(M_k = \\left\\{ m_k^1, m_k^2, \\cdots , m_k^{|M_k|}\\right\\} \\) of k-th entity \\(e_k\\) by extracting the embeddings of the"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 5e9bd356-c275-4a3e-a2d5-571f14bc26ed
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 21696, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "m_k^2, \\cdots , m_k^{|M_k|}\\right\\} \\) of k-th entity \\(e_k\\) by extracting the embeddings of the start special token of every entity in the H.4.2 Coreference resolutionOur model performs the coreference resolution (CR) to capture the interactions between the long-distance mentions in multiple sentences. We define \\(\\mathbf {C^{\\mathcal {Z}_E}}\\), the set of all possible pair-combinations from all entity mention embeddings \\(\\mathcal {Z}_E = \\left\\{ M_1 \\cup M_2 \\cup ... \\cup M_{|E|} \\right\\} \\).$$\\begin{aligned} \\mathbf {C^{\\mathcal {Z}_E}} = \\{ (m_1^1, m_1^2), (m_1^1, m_1^3),..., (m_i^a, m_j^b),...,(m_{|M_E|}^{|M_E|-1}, m_{|M_E|}^{|M_E|})\\} \\end{aligned}$$"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: ba71d233-e0fb-472b-b350-2bb964892f2b
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 22383, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(1)\n                We obtain the probability of whether the two mention embeddings represent the same entity:$$\\begin{aligned} P^{CR} = \\textrm{sigmoid}(\\overline{\\textbf{C}} \\textbf{W}^{CR} + \\textbf{b}^{CR}), \\end{aligned}$$\n                    (2)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: dc69bfd3-fccf-4d4f-a4c0-3dfb0c984725
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 22631, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(2)\n                where \\(\\textbf{C}\\) is the entity mention embedding pair in \\(\\mathbf {C^{\\mathcal {Z}_E}}\\) and \\((\\overline{ \\cdot })\\) is the concatenation operation. \\(\\textbf{W}^{CR}\\) \\(\\in \\) \\(\\mathrm {\\mathbb {R}}^{dim \\times 2}\\) is a weight matrix for binary classification. \\(\\textbf{b}^{CR}\\) is the bias of CR.Since entities have a small number of mentions, each mention pairs from the combinations is rarely co-referencedFootnote 9. Due to the class imbalance, we apply the focal loss\u00a0[36] which is known as a loss function that is robust to the class imbalance issue for \\(\\mathcal {L}^{CR}\\) :$$\\begin{aligned} \\mathcal {L}^{CR}= & {} -\\biggl (y^{CR}(1-P^{CR})^{\\gamma ^{CR}}\\textrm{log}{P^{CR}}\\nonumber \\\\{} & {} + (1-y^{CR})(P^{CR})^{\\gamma ^{CR}}\\textrm{log}(1-P^{CR}) \\biggr ) Q^{CR}, \\end{aligned}$$\n                    (3)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: a634bae9-3cea-4d5a-b000-2f08d2b34298
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 23479, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(3)\n                where \\(y^{CR}\\) is 1 if mention pair refers to the same entity, otherwise 0. \\(Q^{CR}\\) is the class weight vector obtained by reversing the ratio in \\(y^{CR}\\), which is introduced in previous research[36]. \\(\\gamma ^{CR}\\) is the hyperparameter.4.3 Named Entity PredictionWe perform the named entity prediction (NEP) task to let the model comprehend head and tail with named entity-type information. We integrate all mention embeddings of the entity with logsumexp pooling\u00a0[37] to obtain head and tail embeddings as in (4):$$\\begin{aligned} v (e_{k}) = \\textrm{log} \\sum _i^{M_{k}} \\textrm{exp}(m_{k}^i) \\end{aligned}$$\n                    (4)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 1ad84294-038a-47c1-ac70-5824050d8e10
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 24142, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(4)\n                Since head and tail are given, the model calculates the probability \\(P^{NEP}\\) of each named entity-type with the corresponding entity mention embeddings \\(v (\\texttt{head})\\) and \\(v (\\texttt{tail})\\):$$\\begin{aligned} P^{NEP}_{\\texttt {head}} =\\textrm{softmax}(\\textrm{tanh}(v (\\texttt{head})) \\textbf{W}^{NEP} + \\textbf{b}^{NEP}) \\nonumber \\\\ P^{NEP}_{\\texttt {tail}} =\\textrm{softmax}(\\textrm{tanh}(v (\\texttt{tail})) \\textbf{W}^{NEP} + \\textbf{b}^{NEP}), \\end{aligned}$$\n                    (5)\n                where \\(\\textbf{W}^{NEP} \\in \\mathrm {\\mathbb {R}}^{dim \\times |nep|}\\) and |nep| is the number of named entity-type set. \\({\\textbf{b}}^{NEP}\\) is the bias of NEP task.We compute the NEP loss \\(\\mathcal {L}^{NEP}\\) through cross-entropy loss:$$\\begin{aligned} \\mathcal {L}^{NEP} = -\\biggl (y_{\\texttt{head}}^{NEP} \\textrm{log}(P_{\\texttt{head}}^{NEP}) + y_{\\texttt{tail}}^{NEP} \\textrm{log}(P_{\\texttt{tail}}^{NEP}) \\biggr ), \\end{aligned}$$"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 7b573c94-3a18-40f2-accf-d5b4841aae92
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 25142, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(6)\n                where \\(y_{\\texttt{head}}^{NEP}\\) and \\(y_{\\texttt{tail}}^{NEP}\\) are a ground-truth named entity-type of head and tail, respectively.Table 4 RE results (\\(\\%\\)) of different RE models on  TREK  datasetFull size table4.4 Relation extractionFor the relation extraction task, we model the interactions between the mention embeddings as in (7).$$\\begin{aligned} V(\\texttt{head})&= \\textrm{tanh} (v(\\texttt{head}) \\textbf{W}_{\\texttt{head}}), \\nonumber \\\\ V(\\texttt{tail})&= \\textrm{tanh} (v(\\texttt{tail}) \\textbf{W}_{\\texttt{tail}}) \\end{aligned}$$\n                    (7)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 6fcb0828-32c9-4c5f-8b7b-dbba9a872144
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 25729, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(7)\n                Inspired by previous research[10], we employ group bilinear classifier\u00a0[38, 39] to reduce the number of parameters when utilizing vanilla bilinear classifier. Therefore, we divide the entity embeddings into block size \\(\\alpha \\) as in (8):$$\\begin{aligned} {V(\\texttt{head})} = [{V(\\texttt{head})}_1;...;{V(\\texttt{head})}_\\alpha ] \\nonumber \\\\ {V(\\texttt{tail})} = [{V(\\texttt{tail})}_1;...;{V(\\texttt{tail})}_\\alpha ] \\end{aligned}$$\n                    (8)\n                Afterward, we apply group bilinear operation as indicated in (9):$$\\begin{aligned} P^{RE}_{r} = \\textrm{sigmoid} \\left( \\sum _{i=1}^\\alpha {V(\\texttt{head})}^\\text {T}_{i\\intercal } {\\textbf{W}}_i^{RE} {V(\\texttt{tail})}_i + \\textbf{b}^{RE}\\right) \\end{aligned}$$\n                    (9)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: cef1d27d-45e4-4655-8f82-c974a1635f7b
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 26530, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "where \\({\\textbf{W}}_i^{RE}\\in \\mathrm {\\mathbb {R}}^{dim/\\alpha \\times dim/\\alpha }\\) is the weight matrix of the operation and \\(\\alpha \\) is the hyperparameter. \\({\\textbf{b}}^{RE}\\) is the bias of RE task.Additionally, we utilize adaptive threshold loss\u00a0[10] to consider the multiple relations that can exist between two entities in prediction. The adaptive threshold loss is used to obtain an entity-dependent threshold value with the learnable threshold class TH. It is tuned to maximize the evaluation scores in the inference stage, thereby returning predicted relations label above the threshold TH, otherwise deciding there is no relation.For the loss function \\(\\mathcal {L}^{RE}\\), we apply binary cross entropy loss for training as below and split the relation labels into \\(\\mathcal {O}^+\\) and \\(\\mathcal {O}^-\\):$$\\begin{aligned} \\mathcal {L}^{RE}= & {} - \\biggl (\\sum _{r\\in \\mathcal {O}^+_r} \\textrm{log}\\biggl (\\frac{\\textrm{exp}(P^{RE}_r)}{\\sum _{r'\\in \\mathcal"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 6cd0baca-3a0d-4844-a7d0-e8157cba87d3
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 27414, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "_{r\\in \\mathcal {O}^+_r} \\textrm{log}\\biggl (\\frac{\\textrm{exp}(P^{RE}_r)}{\\sum _{r'\\in \\mathcal {O}^+_r\\cup \\left\\{ TH\\right\\} } \\textrm{exp}(P^{RE}_{r'})} \\biggl )\\nonumber \\\\{} & {} + \\textrm{log} \\biggl ( \\frac{\\textrm{exp}(P^{RE}_{TH})}{\\sum _{r'\\in \\mathcal {O}^-_r\\cup \\left\\{ TH\\right\\} } \\textrm{exp}(P^{RE}_{r'})} \\biggl )\\biggl ), \\end{aligned}$$"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: d40dbb53-fc39-4683-b320-0122dd16955c
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 27792, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(10)\n                where \\(\\mathcal {O}^+\\) are positive relation classes that exist between head and tail and \\(\\mathcal {O}^-\\) are negative relation classes that do not exist between two entities.4.5 Final training objectiveConsequently, we obtain the final loss \\(\\mathcal {L}^{total}\\) by integrating losses with task-specific weights, \\(\\eta ^{CR}\\), \\(\\eta ^{NEP}\\), and \\(\\eta ^{RE}\\).$$\\begin{aligned} \\mathcal {L}^{total} = \\eta ^{CR}\\mathcal {L}^{CR} + \\eta ^{NEP}\\mathcal {L}^{NEP} + \\eta ^{RE}\\mathcal {L}^{RE} \\end{aligned}$$\n                    (11)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0d027504-0d64-4fce-aa5b-c056a2fb39b3
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 28375, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "5 Experiments5.1 Experimental SetupThe evaluation metrics are the F1 and Ign F1 scores. Ign F1 score is an evaluation metric that is computed by excluding the triples which are included in the train set, supplementing the F1 score in unseen data.For hyperparameters, we set the learning rate as 5e-5 with AdamW\u00a0[40] optimizer, and then the learning rate is linearly decayed. The batch size and sequence length are set as 2 and 512, respectively. The whole model was trained for 30 epochs on 4 RTX A6000 GPUs. The average training time of TREKER for each PLM is 21 hours. Following previous document-level RE research in English \u00a0[24], we set \\(\\alpha \\) as 64 and \\(\\gamma ^{CR}\\) as 2. The task weight \\(\\eta ^{CR}\\), \\(\\eta ^{ET}\\), and \\(\\eta ^{RE}\\) are set as 0.1, 0.1, and 1, respectively and we manually searched for the parameters.As baselines, we adopt the base version of BERT-multilin-gual\u00a0[41], KoBERT(SKT Brain, 2019), KLUE-BERT\u00a0[29], and KLUE-RoBERTa\u00a0[29] from"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 02939dee-6b38-4232-9ee2-e46bb04d8354
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 29254, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "of BERT-multilin-gual\u00a0[41], KoBERT(SKT Brain, 2019), KLUE-BERT\u00a0[29], and KLUE-RoBERTa\u00a0[29] from huggingface\u00a0[42]. The baseline models were presented with the previous Korean language understanding evaluation benchmark.Table 5 Ablation studies of our model on  TREK  dataset. The results are predicted from TREKER\\(_{\\text {KLUE-RoBERTa}}\\). Avg. Dec indicates the average of performance decrease between the baseline and our model from both Ign F1 and F1Full size table5.2 Main resultsWe compare TREKER models with the baselines and they all show improved performance as demonstrated in Table\u00a04. TREKER\\(_{\\text {RoBERTa}}\\) achieves the best performance among other TREKER models. In particular, TREKER\\(_{\\text {KoBERT}}\\) achieves the largest improvements of 6.02%p and the result implies that our method can be applied regardless of the type of language model. Also, these substantial improvements show that the learning objectives with consideration of the named entity-types lead to the"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0b1936e1-2146-4748-ba37-498d64ce6cfa
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 30156, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "show that the learning objectives with consideration of the named entity-types lead to the effective relation prediction in given documents.We observe the performance differences between Korean and multilingual language models. TREKER\\(_{\\text {KLUE-RoBERTa}}\\) and TREKER\\(_{\\text {KLUE-BERT}}\\) outperform performances of TREKER\\(_{\\text {BERT-multilingual}}\\), respectively. The gaps demonstrate that the TREKER\\(_{\\text {KLUE-RoBERTa}}\\) and TREKER\\(_{\\text {KLUE-BERT}}\\) models generally understand better the Korean context than TREKER\\(_{\\text {BERT-multilingual}}\\) model which is trained in various languages.Meanwhile, it is shown that TREKER\\(_{\\text {KoBERT}}\\) model demonstrate lower performances than those of TREKER\\(_{\\text {BERT-multilingual}}\\). We assume that the size of the vocabulary and pre-trained corpus of the model affect performances. KLUE-BERT and KLUE-RoBERTa\u00a0[29] are trained on 63G sentences with the size of 32K vocabulary, whereas KoBERT\u00a0(SKT Brain, 2019) is"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 55c5c00c-277d-4560-b3fd-856a48dac4a1
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 31053, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "are trained on 63G sentences with the size of 32K vocabulary, whereas KoBERT\u00a0(SKT Brain, 2019) is trained on 5M sentences has the size of 8,002 vocabulary. Therefore, our  TREK  dataset is challenging if the language model\u2019s understanding capability is limited.5.3 Ablation studiesTable\u00a05 presents the ablation results of the proposed model. TREKER\\(_{\\text {KLUE-RoBERTa}}\\) is validated by excluding the CR and NEP tasks. When the CR and NEP are both removed, the performance decreases by 3.49% on average in the Ign F1 and F1, respectively. When the CR task is removed, there is an average decrease of 0.79%, respectively. Similarly, the score drops when the TREKER is not trained on NEP task. These findings indicate that considering the named entity-type information significantly contributes to the accurate prediction of relations.Fig. 6Comparison of relation prediction results between the baselines and our modelsFull size image6 Qualitative analysisWe conduct the qualitative analysis to"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 091b96eb-ac1c-431d-b5f7-b5e381140691
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 31951, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "baselines and our modelsFull size image6 Qualitative analysisWe conduct the qualitative analysis to demonstrate how each model understands the relation between entities in Korean document-level RE task. Figure 6 shows the relation prediction results obtained from KLUE-RoBERTa and TREKER\\(_{\\text {KLUE-RoBERTa}}\\). In sentences [1] and [2],"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: d8816b31-3652-4f78-912f-463a9a05a7b4
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 32294, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(Natural IT Industry Promotion Agency) and \n(Seoul) are given. The gold relation of the enti-ties is \u2018org:member_of\u2019. In the prediction of KLUE-RoBERTa, incorrect relation \u2018org:founded_by\u2019 is suggested, while TREKER\\(_{\\text {KLUE-RoBERTa}}\\) correctly finds \u2018org:member_of\u2019.We attribute these results to that TREKER\\(_{\\text {KLUE-RoBERTa}}\\) understands the characteristics of the Korean language more precisely than KLUE-RoBERTa does. TREKER\\(_{\\text {KLUE-RoBERTa}}\\) is more effective in distinguishing the functional morpheme \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 00bd2fbc-76b4-4e01-945d-30aa29161636
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 32829, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "[-eun]\u201d and"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: aa38846c-3854-4b4b-8401-ae3b9ffb512b
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 32842, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(National IT Industry Promotion Agency) and is shown to predict the ground-truth relation. The result is compatible with the experiments in Section\u00a05.2.7 LimitationIn this work, we introduce the document-level RE dataset in Korean. We believe that our  TREK  dataset will contribute to the RE research in Korean, but there are still some limitations that can be improved in future work. Since we did not use all of the documents from NAVER encyclopedia, the scalability of the dataset is relatively lower than that of DocRED\u00a0[9]. However, our dataset is the largest document-level RE dataset in Korean, and we plan to build a larger dataset in future work. Another limitation of our data construction process is the necessity of human inspection. We aim to address this by either using documents where the subject missing sentence does not exist or refining our NER and RE modules to reduce error cases, ultimately aiming to construct a quality-ensured dataset without the need for human"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: b0bf5dd0-900c-4c14-bcd3-6e7e2eb8f8bf
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 33733, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "error cases, ultimately aiming to construct a quality-ensured dataset without the need for human inspection.8 ConclusionIn this paper, we introduced the  TREK  dataset, which is the first large-scale Korean document-level RE dataset built from high-quality Korean encyclopedia documents. We constructed  TREK  dataset in a distantly-supervised manner for labor- and cost-effectiveness and conducted a human inspection for ensured quality. Moreover, we showed the detailed analyses on our  TREK  dataset and conduct the human evaluation. Furthermore, we proposed a model that utilizes named entity-type information and adopts the properties of the Korean language. In experiments, our proposed models outperformed the baselines, and ablation studies were also conducted to show our method\u2019s effectiveness. The qualitative results showed the model\u2019s enhanced capability. It is expected that our  TREK  dataset can contribute to the field of Korean document-level RE, bringing out various research in"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: cc8bd53e-bd44-40b2-a572-934a26a0f16d
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 34633, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "dataset can contribute to the field of Korean document-level RE, bringing out various research in regard to both data construction and the development of ultimate Korean RE models in the future. requirements."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 64fcad86-3d28-42bf-9e97-639d763b1ad9
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 34843, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Data availability and access\nThe  TREK  dataset generated during the current study is available at https://github.com/sonsuhyune/TREKER. Other data that support the findings of this study are available from the corresponding author upon reasonable request.\nNotes\u2018()\u2019 is a translation of the word and \u2018[]\u2019 indicates the pronunciation of functional morpheme. The functional morphemes such as \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 99469d58-3dae-4e0b-a0f8-3290af8b0a28
encoding: FLOAT32
metadata:
{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 35236, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "[-neun]\u201d are cannot be translated directly to the English word.Our code and the dataset are publicly accessible at https://github.com/sonsuhyune/TREKER.The statistics of the datasets\u00a0[29, 30] are presented in Table\u00a02.https://terms.naver.com/The details about the characteristic of NAVER encyclopedia is in Appendix A.National Institute of the Korean Language, KLUE Github, Wikimedia DumpOur NER model achieves F1 score of 88.64.Our sentence-level RE model shows F1 score of 82.88.In  TREK  dataset, each entity has 1.61 mentions on average.https://terms.naver.com/ReferencesHendrickx I, Kim SN, Kozareva Z, Nakov P, S\u00e9aghdha D\u00d3, Pad\u00f3 S, Pennacchiotti M, Romano L, Szpakowicz S (2010) Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In: Proceedings of the 5th International Workshop on Semantic Evaluation, pp. 33\u201338Shen Y, Huang XJ (2016) Attention-based convolutional neural network for semantic relation extraction. In: Proceedings of COLING 2016,"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: fb346acb-a736-450d-aa5f-a2a5d311a802
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created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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created_at: 2024-12-17 10:21:24
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created_at: 2024-12-17 10:21:24
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created_at: 2024-12-17 10:21:24
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 47696, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "PubMed\u00a0Google ScholarContributionsAll authors contributed to the study\u2019s conception and design. Suhyune Son performed material preparation, experiments, and writing the manuscript. Jungwoo Lim and Seonmin Koo performed the experiments and analyzed the results. Jinsung Kim developed the model. Younghoon Kim, Youngsik Lim, and Dongseok Hyun performed data collection and material preparation. Heuiseok Lim commented on previous versions of the manuscript. All authors read and approved the final manuscript.Corresponding authorCorrespondence to\n                Heuiseok Lim.Ethics declarations\nCompeting interests\nThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\nEthical and informed consent for data used"}


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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 48481, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Ethical and informed consent for data used\nThe authors declare that they have no conflict of interest regarding the research carried out and the data produced. The authors are aware of and consent to the publication of data resulting from this research."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 48735, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Additional informationPublisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.AppendicesAppendix A: Examples of NAVER Encyclopedia DocumentsAs illustrated in Table 6, most of the sentences in the raw documents do not have a subject which is a document\u2019s title. Since NAVERFootnote 10 encyclopedia documents concentrate the title of the document directly, most of the sentences naturally omit the subjects. The red tag indicates the place for the subject.Table 6 Subject missing in Naver Encyclopedia Documents. [SubM] indicates subject missing in each sentence.Full size tableAppendix: B TREK dataset construction process in koreanThe process of  TREK  dataset construction is in Fig. \u00a07.Fig. 7The process of  TREK  dataset construction. In phase 1 and 2, the word \u201c"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 49581, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "(the Holy See)\u201d marked in blue indicates the subject inserted by pseudo-subject insertion. The highlighted texts and solid green lines are the named entities and relations, respectively. In phase 3, the dashed gray indicate deleted relation and the red lines indicate re-assigned relationsFull size imageAppendix: C Details of human inspectionHuman Annotation We employed 32 individuals who are native speakers of Korean and hold at least a bachelor\u2019s degree. Workers annotate relations of non-subject entities and are provided sufficient explanations on the task. Each worker has been fairly compensated at a rate of $4.5 per single document. The expected productivity is completing 2-3 documents per hour, ensuring a minimum compensation of $10 per hour. It is worth noting that the minimum hourly wage in South Korea for 2023 is $8. The user interface can be found in Fig.\u00a08.Fig. 8Interface for human annotationFull size imageFig. 9Interface for human filteringFull size imageHuman Filtering In"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 50479, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "human annotationFull size imageFig. 9Interface for human filteringFull size imageHuman Filtering In order to enhance the quality of the dataset, we conducted human filtering. To eliminate incorrect examples, we employed 29 workers under the same conditions as mentioned earlier. Workers evaluated the validity of the examples, and three individuals assessed each example. All workers have received reasonable monetary compensation; $4 per single document. All workers are expected to finish 2\u00a03 documents in one hour, resulting in the minimum compensation is $8.5 per hour. The user interface can be found in Fig.\u00a09.Appendix: D Examples of TREK datasetFig. 10Examples from  TREK  dataset. For clarity, named entities involved in relations are colored in blue or green, and other named entities are underlined. We also provide the named entity-typeFull size imageAppendix: E Distribution of each dataset split in TREK datasetFig. 11Distribution of train set. (a) is the named entity-type distribution"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: e7a307b4-464f-4f5a-9b09-455942781957
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 51382, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "split in TREK datasetFig. 11Distribution of train set. (a) is the named entity-type distribution and (b) is the relation class distributionFull size imageFig. 12Distribution of development set. (a) is the named entity-type distribution and (b) is the relation class distributionFull size imageFig. 13Distribution of test set. (a) is the named entity-type distribution and (b) is the relation class distributionFull size imageAppendix: F Named Entity-TypeTable 7 Named entity-type descriptionsFull size tableAppendix: G Relation typeTable 8 Descriptions of 26 relation classesFull size tableRights and permissions"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 51995, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Open Access  This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article\u2019s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article\u2019s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 52847, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Reprints and permissionsAbout this articleCite this articleSon, S., Lim, J., Koo, S. et al. A large-scale dataset for korean document-level relation extraction from encyclopedia texts.\n                    Appl Intell 54, 8681\u20138701 (2024). https://doi.org/10.1007/s10489-024-05605-9Download citationAccepted: 08 June 2024Published: 02 July 2024Issue Date: September 2024DOI: https://doi.org/10.1007/s10489-024-05605-9Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard\n                            Provided by the Springer Nature SharedIt content-sharing initiative\n                        KeywordsNatural Language ProcessingInformation ExtractionDocument-level Relation ExtractionKorean Relation Extraction"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://link.springer.com/article/10.1007/s10489-024-05605-9", "title": "A large-scale dataset for korean document-level relation extraction from encyclopedia texts | Applied Intelligence\n            ", "description": "Document-level relation extraction (RE) aims to predict the relational facts between two given entities from a document. Unlike widespread research on docu", "language": "en", "start_index": 53705, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Use our pre-submission checklist\n\n\nAvoid common mistakes on your manuscript.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdvertisement\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSearch\n\n\n\nSearch by keyword or author\n\n\n\n\n\n\nSearch\n\n\n\n\n\n\n\nNavigation\n\n\n\n                        Find a journal\n\t\t\t\t\t\t\t\t\t \n\n\n\n                        Publish with us\n\t\t\t\t\t\t\t\t\t \n\n\n\n                        Track your research\n\t\t\t\t\t\t\t\t\t \n\n\n\n\n\n\n\n\nDiscover content\n\nJournals A-Z\nBooks A-Z\n\n\n\nPublish with us\n\nJournal finder\nPublish your research\nOpen access publishing\n\n\n\nProducts and services\n\nOur products\nLibrarians\nSocieties\nPartners and advertisers\n\n\n\nOur imprints\n\nSpringer\nNature Portfolio\nBMC\nPalgrave Macmillan\nApress\n\n\n\n\n\n\n\n\nYour privacy choices/Manage cookies\n\n\nYour US state privacy rights\n\n\nAccessibility statement\n\n\nTerms and conditions\n\n\nPrivacy policy\n\n\nHelp and support\n\n\nCancel contracts here\n\n\n\n\n\n125.240.175.68\n\nNot affiliated\n\n\n\n\n\u00a9 2024 Springer Nature"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: a54da86b-c0a5-4a02-9653-dc6a870ee1ec
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 55, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "OCR actions reference - Power Automate | Microsoft Learn\n\n\n\n\nSkip to main content\n\n\nThis browser is no longer supported.\nUpgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support.\n\nDownload Microsoft Edge\nMore info about Internet Explorer and Microsoft Edge\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable of contents\n\n\n\nExit focus mode\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRead in English\n\n\n\n\nSave\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTable of contents\n\n\n\nRead in English\n\n\n\n\nSave\n\n\n\n\n\nAdd to Plan\n\n\n\n\nEdit\n\n\nShare via\n\n\n\n\nFacebook\n\n\n\n\n\nx.com\n\n\n\n\n\nLinkedIn\n\n\n\n\n\nEmail\n\n\n\n\n\nPrint\n\n\n\n\n\n\n\n\n\nTable of contents\n\nOCR actions\n\nArticle12/16/2022\n\n9 contributors\n\n\n\n\n\n\n\nFeedback"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 618, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Print\n\n\n\n\n\n\n\n\n\nTable of contents\n\nOCR actions\n\nArticle12/16/2022\n\n9 contributors\n\n\n\n\n\n\n\nFeedback\n\n\nIn this article\nPower Automate enables users to read, extract, and manage data within files through optical character recognition (OCR).\nTo create an OCR engine and extract text from images and documents, use the Extract text with OCR action. The following example extracts text from the entire specified image.\n\nAll OCR actions can create a new OCR engine variable or use an existing one. You can use existing OCR engine variables in any action that offers OCR capabilities.\nPower Automate supports the Windows OCR and Tesseract engines. To configure the selected OCR engine, navigate to the OCR engine settings of the appropriate action. The available options include the language and the image width and height multipliers.\n\nNote"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 1445, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Note\n\nAll the available OCR engines are pre-installed in Power Automate and work locally without connecting to the cloud. However, you may need to download language packs or data files to extract texts in specific languages.\nImage multipliers increase the image size to make searching and text extraction more effective. Setting values greater than three may lead to erroneous results."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 1833, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Use the Windows OCR engine\nThe default OCR engine in Power Automate is the Windows OCR engine. To extract texts using the Windows OCR engine, you must install the appropriate language pack for the language you want to extract.\nIf the appropriate language pack isn't installed, Power Automate throws an error, prompting you to install it. To find more information regarding downloading and installing language packs, go to Language packs for Windows.\nAfter installing the appropriate language pack, extend the OCR engine settings of the OCR action and select the language you want. The Windows OCR engine supports 25 languages, including Chinese (Simplified and Traditional), Czech, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian (Cyrillic and Latin), Slovak, Spanish, Swedish, and Turkish.\nUse the Tesseract OCR engine"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 28193444-6cae-44e2-98a7-a23d48da66be
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 2758, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Note\nTo make use of the Tesseract OCR engine, make sure the machine's CPU supports AVX2 instruction set."}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 2864, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Apart from the Windows OCR engine, Power Automate supports the Tesseract engine. This engine can extract text in five languages without further configuration: English, German, Spanish, French, and Italian.\nTo extract text in a language outside the mentioned list, enable the Use other languages option in the OCR engine settings of the OCR action. When this option is enabled, the action displays two more parameters: Language abbreviation and Language data path.\nThe Language abbreviation field indicates to the engine which language to look for during OCR. The Language data path field contains the language data files (.traineddata) used to train the OCR engine. You can find the language data files for all the available languages in this GitHub repository.\nYou can also use the Tesseract engine to extract text from multilingual documents. To find more information regarding extracting text from multilingual documents, go to Perform OCR on multilingual documents.\n If text on screen (OCR)"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 3835, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "If text on screen (OCR)\nMarks the beginning of a conditional block of actions depending on whether a given text appears on the screen or not, using OCR.\nInput parameters"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 701dc06a-9a4d-41e7-8ab6-c2f5243841aa
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 4008, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Argument\nOptional\nAccepts\nDefault Value\nDescription\n\n\n\n\nIf text\nN/A\nExists, Doesn't exist\nExists\nSpecifies whether to check if the text exists or not on the given source to analyze\n\n\nOCR engine type\nNo\nWindows OCR engine, Tesseract engine, OCR engine variable\nOCR engine variable\nThe OCR engine type to use. Select a preconfigured OCR engine or set up a new one.\n\n\nOCR engine variable\nNo\nOCREngineObject\n\nThe engine to use for the OCR operation\n\n\nText to find\nNo\nText value\n\nThe text to search for in the specified source\n\n\nIs regular expression\nN/A\nBoolean value\nFalse\nSpecifies whether to use a regular expression to find the specified text\n\n\nSearch for text on\nN/A\nEntire screen, Foreground window\nEntire screen\nSpecifies whether to search for the specified text on the entire visible screen or just the foreground window"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 4835, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Search mode\nN/A\nWhole of specified source, Specific subregion only, Subregion relative to image\nWhole of specified source\nSpecifies whether to scan the entire screen (or window) or a narrowed down subregion of it\n\n\nImage(s)\nNo\nList of Images\n\nThe image(s) specifying the subregion (relative to the top left corner of the image) to scan for the supplied text\n\n\nX1\nYes\nNumeric value\n\nThe start X coordinate of the subregion to scan for the supplied text\n\n\nTolerance\nYes\nNumeric value\n10\nSpecifies how much the image(s) searched for can differ from the originally chosen image\n\n\nY1\nYes\nNumeric value\n\nThe start Y coordinate of the subregion to scan for the supplied text\n\n\nX1\nYes\nNumeric value\n\nThe start X coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nX2\nYes\nNumeric value\n\nThe end X coordinate of the subregion to scan for the supplied text\n\n\nY1\nYes\nNumeric value"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 7ad3095c-8bfb-4bc3-bf84-ae5e3faa034a
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 5653, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "The end X coordinate of the subregion to scan for the supplied text\n\n\nY1\nYes\nNumeric value\n\nThe start Y coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nY2\nYes\nNumeric value\n\nThe end Y coordinate of the subregion to scan for the supplied text\n\n\nX2\nYes\nNumeric value\n\nThe end X coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nY2\nYes\nNumeric value\n\nThe end Y coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nWindows OCR language\nN/A\nChinese (Simplified), Chinese (Traditional), Czech, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian (Cyrillic), Serbian (Latin), Slovak, Spanish, Swedish, Turkish\nEnglish\nThe language of the text that the Windows OCR engine detects"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 6552, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Use other language\nN/A\nBoolean value\nFalse\nSpecifies whether to use a language not given in the 'Tesseract language' field\n\n\nTesseract language\nN/A\nEnglish, German, Spanish, French, Italian\nEnglish\nThe language of the text that the Tesseract engine detects\n\n\nLanguage abbreviation\nNo\nText value\n\nThe Tesseract abbreviation of the language to use. For example, if the data is 'eng.traineddata', set this parameter to 'eng'\n\n\nLanguage data path\nNo\nText value\n\nThe path of the folder that holds the specified language's Tesseract data\n\n\nImage width multiplier\nNo\nNumeric value\n1\nThe width multiplier of the image\n\n\nImage height multiplier\nNo\nNumeric value\n1\nThe height multiplier of the image\n\n\nImage matching algorithm\nN/A\nBasic, Advanced\nBasic\nWhich image algorithm to use when searching for image\n\n\n\n\nNote"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
id: 0a670bb5-6004-42df-b8eb-a490d09cf8de
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 7353, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Note\n\nPower Automate's regular expression engine is .NET. To find more information about regular expressions, go to Regular Expression Language - Quick Reference.\nThe OCR engine variable option is planned for deprecation.\n\n\nVariables produced\n\n\n\nArgument\nType\nDescription\n\n\n\n\nLocationOfTextFoundX\nNumeric value\nThe X coordinate of the point where the text appears on the screen. If the search is performed in the foreground window, the coordinate returned is relative to the top left corner of the window\n\n\nLocationOfTextFoundY\nNumeric value\nThe X coordinate of the point where the text appears on the screen. If the search is performed in the foreground window, the coordinate returned is relative to the top left corner of the window\n\n\n\n Exceptions\n\n\n\nException\nDescription\n\n\n\n\nCan't check if text exists in non-interactive mode\nIndicates that it isn't possible to check for the text on the screen when in non-interactive mode"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 8284, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Invalid subregion coordinates\nIndicates that the specified subregion coordinates are invalid\n\n\nFailed to analyze text with OCR\nIndicates an error occurred while trying to analyze the text using OCR\n\n\nFailed to create the OCR engine\nIndicates an error occurred while trying to create the OCR engine\n\n\nData path folder doesn't exist\nIndicates that the folder specified for the language data doesn't exist\n\n\nThe selected Windows language pack isn't installed on the machine\nIndicates that the selected Windows language pack hasn't been installed on the machine\n\n\nOCR engine not alive\nIndicates that the OCR engine isn't alive\n\n\n\n Wait for text on screen (OCR)\nWait until a specific text appears/disappears on the screen, on the foreground window, or relative to an image on the screen or foreground window using OCR.\nInput parameters\n\n\n\nArgument\nOptional\nAccepts\nDefault Value\nDescription\n\n\n\n\nWait for text to\nN/A\nAppear, Disappear\nAppear\nSpecifies whether to wait for the text to appear or disappear"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 9284, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "OCR engine type\nNo\nWindows OCR engine, Tesseract engine, OCR engine variable\nOCR engine variable\nThe OCR engine type to use. Select a preconfigured OCR engine or set up a new one.\n\n\nOCR engine variable\nNo\nOCREngineObject\n\nThe engine to use for the OCR operation\n\n\nText to find\nNo\nText value\n\nThe text to search for in the specified source\n\n\nIs regular expression\nN/A\nBoolean value\nFalse\nSpecifies whether to use a regular expression to find the specified text\n\n\nSearch for text on\nN/A\nEntire screen, Foreground window\nEntire screen\nSpecifies whether to search for the specified text on the entire visible screen or just the foreground window\n\n\nSearch mode\nN/A\nWhole of specified source, Specific subregion only, Subregion relative to image\nWhole of specified source\nSpecifies whether to scan the entire screen (or window) or a narrowed down subregion of it\n\n\nImage(s)\nNo\nList of Images"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 10143, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Image(s)\nNo\nList of Images\n\nThe image(s) specifying the subregion (relative to the top left corner of the image) to scan for the supplied text\n\n\nX1\nYes\nNumeric value\n\nThe start X coordinate of the subregion to scan for the supplied text\n\n\nTolerance\nYes\nNumeric value\n10\nSpecifies how much the image(s) searched for can differ from the originally chosen image\n\n\nY1\nYes\nNumeric value\n\nThe start Y coordinate of the subregion to scan for the supplied text\n\n\nX1\nYes\nNumeric value\n\nThe start X coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nX2\nYes\nNumeric value\n\nThe end X coordinate of the subregion to scan for the supplied text\n\n\nY1\nYes\nNumeric value\n\nThe start Y coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nY2\nYes\nNumeric value\n\nThe end Y coordinate of the subregion to scan for the supplied text\n\n\nX2\nYes\nNumeric value"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 10964, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "The end Y coordinate of the subregion to scan for the supplied text\n\n\nX2\nYes\nNumeric value\n\nThe end X coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nY2\nYes\nNumeric value\n\nThe end Y coordinate of the subregion relative to the specified image to scan for the supplied text\n\n\nWindows OCR language\nN/A\nChinese (Simplified), Chinese (Traditional), Czech, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian (Cyrillic), Serbian (Latin), Slovak, Spanish, Swedish, Turkish\nEnglish\nThe language of the text that the Windows OCR engine detects\n\n\nUse other language\nN/A\nBoolean value\nFalse\nSpecifies whether to use a language not given in the 'Tesseract language' field\n\n\nTesseract language\nN/A\nEnglish, German, Spanish, French, Italian\nEnglish\nThe language of the text that the Tesseract engine detects\n\n\nLanguage abbreviation\nNo\nText value"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 11904, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Language abbreviation\nNo\nText value\n\nThe Tesseract abbreviation of the language to use. For example, if the data is 'eng.traineddata', set this parameter to 'eng'\n\n\nLanguage data path\nNo\nText value\n\nThe path of the folder that holds the specified language's Tesseract data\n\n\nImage width multiplier\nNo\nNumeric value\n1\nThe width multiplier of the image\n\n\nImage height multiplier\nNo\nNumeric value\n1\nThe height multiplier of the image\n\n\nImage matching algorithm\nN/A\nBasic, Advanced\nBasic\nWhich image algorithm to use when searching for image\n\n\nFail with timeout error\nN/A\nBoolean value\nFalse\nSpecify whether you want the action to wait indefinitely or fail after a set time period\n\n\n\n\nNote\n\nPower Automate's regular expression engine is .NET. To find more information about regular expressions, go to Regular Expression Language - Quick Reference.\nThe OCR engine variable option is planned for deprecation.\n\n\nVariables produced\n\n\n\nArgument\nType\nDescription"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 12809, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Variables produced\n\n\n\nArgument\nType\nDescription\n\n\n\n\nLocationOfTextFoundX\nNumeric value\nThe X coordinate of the point where the text appears on the screen. If the search is performed in the foreground window, the coordinate returned is relative to the top left corner of the window\n\n\nLocationOfTextFoundY\nNumeric value\nThe X coordinate of the point where the text appears on the screen. If the search is performed in the foreground window, the coordinate returned is relative to the top left corner of the window\n\n\n\n Exceptions\n\n\n\nException\nDescription\n\n\n\n\nCan't check if text exists in non-interactive mode\nIndicates that it isn't possible to check for the text on the screen when in non-interactive mode\n\n\nInvalid subregion coordinates\nIndicates that the specified subregion coordinates are invalid\n\n\nFailed to analyze text with OCR\nIndicates an error occurred while trying to analyze the text using OCR"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 13716, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Failed to create the OCR engine\nIndicates an error occurred while trying to create the OCR engine\n\n\nData path folder doesn't exist\nIndicates that the folder specified for the language data doesn't exist\n\n\nThe selected Windows language pack isn't installed on the machine\nIndicates that the selected Windows language pack hasn't been installed on the machine\n\n\nOCR engine not alive\nIndicates that the OCR engine isn't alive\n\n\nTimeout error\nIndicates that the action failed after a set time period\n\n\n\n Extract text with OCR\nExtract text from a given source using the given OCR engine.\nInput parameters\n\n\n\nArgument\nOptional\nAccepts\nDefault Value\nDescription\n\n\n\n\nOCR engine\nNo\nWindows OCR engine, Tesseract engine, OCR engine variable\nOCR engine variable\nThe OCR engine type to use. Select a preconfigured OCR engine or set up a new one\n\n\nOCR engine variable\nNo\nOCREngineObject\n\nThe engine to use for the OCR operation"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 14551, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "OCR engine variable\nNo\nOCREngineObject\n\nThe engine to use for the OCR operation\n\n\nOCR source\nN/A\nScreen, Foreground window, Image on disk\nScreen\nThe source of the image to perform the OCR operation on\n\n\nImage file path\nNo\nFile\n\nThe path of the image to perform the OCR operation on\n\n\nSearch mode\nN/A\nWhole of specified source, Specific subregion only, Subregion relative to image\nWhole of specified source\nThe selected mode for the OCR operation\n\n\nImage\nNo\nList of Images\n\nThe image to use for narrowing down the scan to a subregion that is relative to the specified image\n\n\nTolerance\nYes\nNumeric value\n10\nSpecifies how much the image can differ from the originally chosen image\n\n\nX1\nYes\nNumeric value\n\nThe start X coordinate of the subregion to narrow down the scan\n\n\nX2\nYes\nNumeric value\n\nThe end X coordinate of the subregion to narrow down the scan\n\n\nY1\nYes\nNumeric value\n\nThe start Y coordinate of the subregion to narrow down the scan\n\n\nY2\nYes\nNumeric value"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 15428, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "The start Y coordinate of the subregion to narrow down the scan\n\n\nY2\nYes\nNumeric value\n\nThe end Y coordinate of the subregion to narrow down the scan\n\n\nWindows OCR language\nN/A\nChinese (Simplified), Chinese (Traditional), Czech, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian (Cyrillic), Serbian (Latin), Slovak, Spanish, Swedish, Turkish\nEnglish\nThe language of the text that the Windows OCR engine detects\n\n\nUse other language\nN/A\nBoolean value\nFalse\nSpecifies whether to use a language not given in the 'Tesseract language' field\n\n\nTesseract language\nN/A\nEnglish, German, Spanish, French, Italian\nEnglish\nThe language of the text that the Tesseract engine detects\n\n\nLanguage abbreviation\nNo\nText value\n\nThe Tesseract abbreviation of the language to use. For example, if the data is 'eng.traineddata', set this parameter to 'eng'\n\n\nLanguage data path\nNo\nText value"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 16367, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Language data path\nNo\nText value\n\nThe path of the folder that holds the specified language's Tesseract data\n\n\nImage width multiplier\nNo\nNumeric value\n1\nThe width multiplier of the image\n\n\nImage height multiplier\nNo\nNumeric value\n1\nThe height multiplier of the image\n\n\nWait for image to appear\nN/A\nBoolean value\nTrue\nSpecifies whether to wait or not for the image to appear on the screen or foreground window\n\n\nTimeout\nNo\nNumeric value\n5\nSpecifies the time to wait for the operation to complete before the action fails\n\n\nImage matching algorithm\nN/A\nBasic, Advanced\nBasic\nWhich image algorithm to use when searching for image\n\n\n\n\nNote\nThe OCR engine variable option is planned for deprecation.\n\nVariables produced\n\n\n\nArgument\nType\nDescription\n\n\n\n\nOcrText\nText value\nThe result after the text extraction\n\n\n\n Exceptions\n\n\n\nException\nDescription\n\n\n\n\nFailed to extract text with OCR\nIndicates an error occurred while trying to extract text with OCR from the given source"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 17335, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Image file not found\nIndicates that the file doesn't exist on the given path\n\n\nLandmark image not found\nIndicates that the landmark image doesn't exist\n\n\nCan't get text from screen in non-interactive mode\nIndicates that it isn't possible to get text from screen when in non-interactive mode\n\n\nFailed to create the OCR engine\nIndicates an error occurred while trying to create the OCR engine\n\n\nData path folder doesn't exist\nIndicates that the folder specified for the language data doesn't exist\n\n\nThe selected Windows language pack isn't installed on the machine\nIndicates that the selected Windows language pack hasn't been installed on the machine\n\n\nOCR engine not alive\nIndicates that the OCR engine isn't alive\n\n\n\n\n\n\n\n\n\nFeedback\n\nWas this page helpful?\n\n\n\n\n\nYes\n\n\n\n\n\nNo\n\n\n\n\nProvide product feedback\n\n\n\n\nAdditional resources\n\n\n\n\n\n\n\n\n\n\n\nCalifornia Consumer Privacy Act (CCPA) Opt-Out Icon\n\n\n\n\n\nYour Privacy Choices\n\n\n\n\n\n\n\n\nTheme\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLight\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDark"}


created_at: 2024-12-17 10:21:24
topic: persistent://default/default/07bad7f9-a38d-434a-86f5-4b771ae65c6f
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{"source": "https://learn.microsoft.com/en-us/power-automate/desktop-flows/actions-reference/ocr", "title": "OCR actions reference - Power Automate | Microsoft Learn", "description": "See all the available OCR actions.", "language": "en-us", "start_index": 18261, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "Theme\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLight\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDark\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHigh contrast\n\n\n\n\n\n\n\n\nPrevious Versions\nBlog\nContributePrivacyTerms of UseTrademarks\u00a9 Microsoft 2024\n\n\n\nAdditional resources\n\n\n\n\nIn this article\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCalifornia Consumer Privacy Act (CCPA) Opt-Out Icon\n\n\n\n\n\nYour Privacy Choices\n\n\n\n\n\n\n\n\nTheme\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLight\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDark\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHigh contrast\n\n\n\n\n\n\n\n\nPrevious Versions\nBlog\nContributePrivacyTerms of UseTrademarks\u00a9 Microsoft 2024"}


created_at: 2024-12-18 04:07:09
topic: persistent://default/default/36ff8794-e1d1-4b7b-b658-494470d54c17
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created_at: 2024-12-18 04:07:09
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created_at: 2024-12-18 04:07:09
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{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 53, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ucd5c\uadfc \ubcc0\uacbd\ucd5c\uadfc \ud1a0\ub860\ud2b9\uc218 \uae30\ub2a5\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 \ucd5c\uadfc \uc218\uc815 \uc2dc\uac01: 2024-12-29 13:54:13239\ud3b8\uc9d1 \uc694\uccad\ud3b8\uc9d1 \uc694\uccadperm:ip\uc774\uae30 \ub54c\ubb38\uc5d0 \ud3b8\uc9d1 \uad8c\ud55c\uc774 \ubd80\uc871\ud569\ub2c8\ub2e4. \ud574\ub2f9 \ubb38\uc11c\uc758 ACL \ud0ed\uc744 \ud655\uc778\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \ud3b8\uc9d1 \uad8c\ud55c\uc774 \ubd80\uc871\ud55c \uacbd\uc6b0 \uc544\ub798\uc758 '\ud3b8\uc9d1 \uc694\uccad' \ubc84\ud2bc\uc73c\ub85c \ud3b8\uc9d1 \uc694\uccad\uc744 \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. [\ud3b8\uc9d1 \uc694\uccad \ub3c4\uc6c0\ub9d0]\ud3b8\uc9d1 \uc694\uccad\ub2eb\uae30\ud1a0\ub860\uc5ed\uc0ac\ubd84\ub9582024\ub144 \ud56d\uacf5\uc0ac\uace0\ud65c\uc8fc\ub85c \uc774\ud0c8\ubb34\uc548\uad70\uc758 \uc0ac\uac74\uc0ac\uace0\uc81c\uc8fc\ud56d\uacf5/\uc0ac\uac74 \uc0ac\uace0\ubcf4\uc789 737/\uc0ac\uac74 \uc0ac\uace0\ub300\ud55c\ubbfc\uad6d\uc758 \ud56d\uacf5\uc0ac\uace0\ud55c\ud0dc\uad00\uacc4 \u00a0 \ud3b8\uc9d1 \ubcf4\ud638\ub41c \ubb38\uc11c\uc785\ub2c8\ub2e4. \ubb38\uc11c\uc758 ACL \ud0ed\uc744 \ud655\uc778\ud558\uc138\uc694.\uc5d0 \ub300\ud55c\uc774 \ubb38\uc11c\uc5d0\uc11c \ud1a0\ub860\uc774 \uc9c4\ud589\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud1a0\ub860 \uc911\uc778 \ub0b4\uc6a9\uc744 \uc77c\ubc29\uc801\uc73c\ub85c \ud3b8\uc9d1\ud560 \uacbd\uc6b0 \uad00\ub828 \uaddc\uc815\uc5d0 \ub530\ub77c \uc81c\uc7ac\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\uc8fc\uc758. \uc0ac\uac74\u00b7\uc0ac\uace0 \uad00\ub828 \ub0b4\uc6a9\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc0ac\uac74 \uc0ac\uace0 \uad00\ub828 \uc11c\uc220 \uaddc\uc815\uc744 \uc720\uc758\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0Jeju Air Flight 2216 Runway Excursion Accident\ubc1c\uc0dd\uc77c2024\ub144 12\uc6d4 29\uc77c 09\uc2dc 03\ubd84[\uc18c\ubc29\uccad(1\ucc28)] (UTC+9 / KST)\uc720\ud615\ud65c\uc8fc\ub85c \uc774\ud0c8, \ub3d9\uccb4 \ucc29\ub959[\ucd94\uc815]\ubc1c\uc0dd \uc704\uce58\ubb34\uc548\uad6d\uc81c\uacf5\ud56d \ud65c\uc8fc\ub85c \uc77c\uc6d0( \ub300\ud55c\ubbfc\uad6d \ud589\uc815\uad6c \uc18d\ub839 \uc804\ub77c\ub0a8\ub3c4 \ubb34\uc548\uad70 \ub9dd\uc6b4\uba74 \ud53c\uc11c\ub9ac 1050)\uae30\uc885Boeing 737-8AS(WL)\ud56d\uacf5\uc0ac\uc81c\uc8fc\ud56d\uacf5\uae30\uccb4 \ub4f1\ub85d \ubc88\ud638HL8088\ucd9c\ubc1c\uc9c0 \ud0dc\uad6d \ud589\uc815\uad6c \uc18d\ub839 \ubc29\ucf55 \uc218\uc644\ub098\ud488 \uacf5\ud56d\ub3c4\ucc29\uc9c0 \ub300\ud55c\ubbfc\uad6d \ud589\uc815\uad6c \uc18d\ub839 \uc804\ub77c\ub0a8\ub3c4 \ubb34\uc548\uad70 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\ud0d1\uc2b9\uc790\uc2b9\uac1d: 175\uba85[\uad6d\uc801]\uc2b9\ubb34\uc6d0: 6\uba85\uc0ac\ub9dd\uc790\uc2b9\uac1d: 175\uba85 \uc804\uc6d0 \uc0ac\ub9dd\uc2b9\ubb34\uc6d0: 4\uba85\uc0dd\uc874\uc790\uc2b9\ubb34\uc6d0: 2\uba85[4][5]1. \uac1c\uc6942. \uc0ac\uace0 \uae30\uccb43. \uc804\uac1c3.1. \uc0ac\uace03.2. \uad6c\uc870 \ubc0f \uc218\uc2b53.2.1. \ud0d1\uc2b9\uac1d \ubc0f \uad6d\uc801 \ud604\ud6693.3. \uc0ac\ub9dd\uc790 \ubd84\uc11d3.4. \ub300\uc7513.4.1. \ud589\uc815\ubd803.4.2. \uc9c0\ubc29\uc790\uce58\ub2e8\uccb43.4.3. \uc81c\uc8fc\ud56d\uacf53.4.4. \ubcf4\uc7894. \uc6d0\uc7784.1. \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c \uac00\ub2a5\uc1314.2. \ub3d9\uccb4 \ucc29\ub959(Belly Landing) \uc2e4\ud328 \uac00\ub2a5\uc1314.3. \ub79c\ub529 \uae30\uc5b4\uc640 \uc720\uc555 \uacc4\ud1b5 \ubb38\uc81c \uac00\ub2a5\uc1314.4. \uc870\uc885\uc0ac \uacfc\uc2e4 \uac00\ub2a5\uc1314.5. \uae30\ud0c0 \uacf5\ud56d\uc758 \ubb38\uc81c\uc8105. \ubc18\uc7516."}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 165756ef-c389-4541-be3b-72df8688149a
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 954, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uac00\ub2a5\uc1314.2. \ub3d9\uccb4 \ucc29\ub959(Belly Landing) \uc2e4\ud328 \uac00\ub2a5\uc1314.3. \ub79c\ub529 \uae30\uc5b4\uc640 \uc720\uc555 \uacc4\ud1b5 \ubb38\uc81c \uac00\ub2a5\uc1314.4. \uc870\uc885\uc0ac \uacfc\uc2e4 \uac00\ub2a5\uc1314.5. \uae30\ud0c0 \uacf5\ud56d\uc758 \ubb38\uc81c\uc8105. \ubc18\uc7516. \uae30\ud0c06.1. \uc778\uba85 \ud53c\ud574 \uad00\ub8287. \uad00\ub828 \ubcf4\ub3c47.1. \ubc29\uc1a17.2. \uc678\uc2e08. \uad00\ub828 \ubb38\uc11c8.1. \uc720\uc0ac \uc0ac\uace09. \ub458\ub7ec\ubcf4\uae301. \uac1c\uc694[\ud3b8\uc9d1][\uc81c\ubcf4] \ubb34\uc548\uacf5\ud56d\uc11c 181\uba85 \ud0dc\uc6b4 \ud56d\uacf5\uae30 \ucc29\ub959 \uc911 \ucda9\ub3cc(KBS News / 2024\ub144 12\uc6d4 29\uc77c)2024\ub144 12\uc6d4 29\uc77c \uc624\uc804 9\uc2dc 3\ubd84\uacbd \uc81c\uc8fc\ud56d\uacf5 2216\ud3b8\uc774 \ud0dc\uad6d \ubc29\ucf55 \uc218\uc644\ub098\ud488 \uacf5\ud56d\uc5d0\uc11c \ucd9c\ubc1c\ud558\uc5ec \ub300\ud55c\ubbfc\uad6d \ubb34\uc548\uad70 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc73c\ub85c \ucc29\ub959 \ub3c4\uc911 \ubd88\uba85\uc758 \uc774\uc720[\ucd94\uc815]\ub85c \ub79c\ub529 \uae30\uc5b4\ub97c \ub0b4\ub9ac\uc9c0 \ubabb\ud574 \ub3d9\uccb4 \ucc29\ub959\uc744 \uc2dc\ub3c4\ud558\ub358 \uc911 \ud65c\uc8fc\ub85c\ub97c \uc774\ud0c8\ud558\uc5ec \ub85c\uceec\ub77c\uc774\uc800\uac00 \uc124\uce58\ub41c \ub454\ub355\uc744 \ub4e4\uc774\ubc1b\uace0 \uae30\uccb4\uac00 \ud3ed\ubc1c\ud55c \ud56d\uacf5 \uc0ac\uace0\ub2e4.2. \uc0ac\uace0 \uae30\uccb4[\ud3b8\uc9d1]\uc0ac\uace0 \ubc1c\uc0dd 11\ub144 \uc804\uc778 2013\ub144 4\uc6d4 12\uc77c, \ub77c\uc774\uc5b8\uc5d0\uc5b4 \uc2dc\uc808 \ubc14\ub974\uc140\ub85c\ub098 \uc5d8 \ud504\ub77c\ud2b8 \uad6d\uc81c\uacf5\ud56d\uc5d0\uc11c \ucd2c\uc601\ub41c \uc0ac\uace0\uae30[7]\uc0ac\uace0 \ubc1c\uc0dd 5\uc77c \uc804\uc778 2024\ub144 12\uc6d4 24\uc77c, \ub098\uac00\uc0ac\ud0a4 \uacf5\ud56d\uc5d0\uc11c \ucd2c\uc601\ub41c HL8088\uc0ac\uace0\uae30\uc758 \uc794\ud574\ub4f1\ub85d\ubd80\ud638\uc81c\uc870\uc0ac\uc0dd\uc0b0\uc9c0\uae30\uc885\uae30\ub839\ucd5c\ucd08 \ube44\ud589\uc77c\ucd5c\ucd08 \uc778\ub3c4\ud56d\uacf5\uc0ac\uc81c\uc8fc\ud56d\uacf5\ub3c4\uc785\uc77cHL8088\ubcf4\uc789\ubbf8\uad6d \uc6cc\uc2f1\ud134 \uc8fc \ub80c\ud134[8]B737-8AS(WL)15\ub144 4\uac1c\uc6d42009\ub1448\uc6d4 19\uc77c \uc544\uc77c\ub79c\ub4dc \ud589\uc815\uad6c \uc18d\ub839 \ub77c\uc774\uc5b8\uc5d0\uc5b4(EI-EFR)2017\ub1442\uc6d4 3\uc77c\uc0ac\uace0\uae30\uc778 HL8088\uc740 2009\ub144 8\uc6d4 19\uc77c\uc5d0 \uccab \ube44\ud589\uc744 \ud55c \ubcf4\uc789 737-800[9] \ud56d\uacf5\uae30\ub85c, \uc0ac\uace0 \ub2f9\uc2dc \uae30\ub839\uc740 15\ub144\uc774\uc5c8\ub2e4. \uc774 \uae30\uccb4\ub294 \ub77c\uc774\uc5b8\uc5d0\uc5b4\uac00 \ucd9c\uace0\ud55c \uae30\uccb4\ub85c, EI-EFR\ub85c \ucd5c\ucd08 \ub4f1\ub85d\ub418\uc5b4 2017\ub144 \ucd08\uae4c\uc9c0 \uc6b4\uc6a9\ub418\ub2e4\uac00 \ub3d9\ub144 2\uc6d4 3\uc77c\uc5d0 \uc81c\uc8fc\ud56d\uacf5\uc73c\ub85c \ub9e4\uac01\ub418\uc5c8\uc73c\uba70 \ub300\ud55c\ubbfc\uad6d \uad6d\uc801 \ub4f1\ub85d\ubc88\ud638\uc778 HL8088\ub85c \uc7ac\ub4f1\ub85d\ub418\uc5c8\ub2e4.\ub610, \uc0ac\uace0\uae30\ub294 \uc0ac\uace0 \ubc1c\uc0dd \uc774\ud2c0 \uc804\uc778 2024\ub144 12\uc6d4 27\uc77c\uc5d0 7C8135\ud3b8\uc73c\ub85c \uc6b4\ud56d\ud558\uba70 \uae30\ub0b4 \ud658\uc790 \ubc1c\uc0dd[10]\uc73c\ub85c \uc778\ud574 \ube44\uc0c1\uc0ac\ud0dc \ucf54\ub4dc\uc778 \uc2a4\ucffd 7700[11]\uc744 \uc120\uc5b8\ud574 \uae30\uc874 \ubaa9\uc801\uc9c0\uc778 \ubca0\uc774\uc9d5\uc774 \uc544\ub2c8\ub77c \uc778\ucc9c\uad6d\uc81c\uacf5\ud56d\uc73c\ub85c \ud68c\ud56d\ud55c \ubc14 \uc788\ub2e4.#3. \uc804\uac1c[\ud3b8\uc9d1]3.1. \uc0ac\uace0[\ud3b8\uc9d1][\uc81c\ubcf4] \ubb34\uc548\uacf5\ud56d\uc11c 181\uba85 \ud0dc\uc6b4 \ud56d\uacf5\uae30 \ucc29\ub959"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: f3c0d057-366f-44e8-a32b-e2492dcc6e1a
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 1855, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uc2a4\ucffd 7700[11]\uc744 \uc120\uc5b8\ud574 \uae30\uc874 \ubaa9\uc801\uc9c0\uc778 \ubca0\uc774\uc9d5\uc774 \uc544\ub2c8\ub77c \uc778\ucc9c\uad6d\uc81c\uacf5\ud56d\uc73c\ub85c \ud68c\ud56d\ud55c \ubc14 \uc788\ub2e4.#3. \uc804\uac1c[\ud3b8\uc9d1]3.1. \uc0ac\uace0[\ud3b8\uc9d1][\uc81c\ubcf4] \ubb34\uc548\uacf5\ud56d\uc11c 181\uba85 \ud0dc\uc6b4 \ud56d\uacf5\uae30 \ucc29\ub959 \uc911 \ucda9\ub3cc(KBS News / 2024\ub144 12\uc6d4 29\uc77c)Flightradar24\uc758 \uc0ac\uace0 \ub2f9\uc2dc \ud56d\uc801 #2024\ub144 12\uc6d4 29\uc77c \ud604\uc9c0 \uc2dc\uac01 \uc624\uc804 2\uc2dc 29\ubd84, \uc0ac\uace0\uae30\ub294 \ubc29\ucf55 \uc218\uc644\ub098\ud488 \uacf5\ud56d\uc5d0\uc11c \uc774\ub959\ud558\uc600\ub2e4.\uc624\uc804 8\uc2dc 54\ubd84\uacbd, \uc0ac\uace0\uae30\ub294 \uad00\uc81c\ud0d1\uc5d0 \ucc29\ub959 \ud5c8\uac00\ub97c \ubc1b\uace0, 01\ubc88 \ud65c\uc8fc\ub85c[12]\ub85c \uc811\uadfc\ud558\uc600\ub2e4. \ucd5c\uc885 \uc811\uadfc \uc911\uc774\ub358 \uc624\uc804 8\uc2dc 57\ubd84\uacbd, \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \uad00\uc81c\ud0d1\uc740 \uc811\uadfc\ud558\ub358 \uc0ac\uace0\uae30\uc5d0 \uc870\ub958 \ucda9\ub3cc \uacbd\uace0\ub97c \uc804\ub2ec\ud588\ub2e4. \uc774\uc5b4\uc11c \uc624\uc804 8\uc2dc 59\ubd84\uacbd, \uc0ac\uace0\uae30 \uae30\uc7a5\uc774 \uba54\uc774\ub370\uc774\ub97c \uc120\uc5b8\ud588\ub2e4. \uccab \ubc88\uc9f8 \ucc29\ub959\uc5d0 \uc2e4\ud328\ud55c \uc0ac\uace0\uae30\ub294 \ucc29\ub959\ubcf5\ud589\ud558\uc600\ub2e4.\uc9c1\ud6c4 9\uc2dc\uacbd, \uad00\uc81c\ud0d1\uc740 \uae30\uc7a5\uc5d0\uac8c \ubc29\ud5a5\uc744 \ubc14\uafb8\uc5b4 19\ubc88 \ud65c\uc8fc\ub85c[13]\ub85c \ucc29\ub959\ud560 \uac83\uc744 \uc81c\uc548\ud588\uace0, \uae30\uc7a5\uc740 \uc774\ub97c \uc218\uc6a9\ud558\uc600\ub2e4. \uc774\ud6c4 \uc0ac\uace0\uae30\ub294 19\ubc88 \ud65c\uc8fc\ub85c\ub85c \ub450 \ubc88\uc9f8 \ucc29\ub959\uc2dc\ub3c4\ub97c \ud558\uc600\uace0, \uacb0\uad6d \uc624\uc804 9\uc2dc 3\ubd84 \ub3d9\uccb4 \ucc29\ub959 \ud6c4, \uc0ac\uace0\uac00 \ubc1c\uc0dd\ud558\uc600\ub2e4.# \uc2ec\uc9c0\uc5b4 \ub79c\ub529 \uae30\uc5b4\uac00 \ub0b4\ub824\uac00\uc9c0 \uc54a\uc544 \uc18d\ub825\ub3c4 \uac70\uc758 \uc904\uc774\uc9c0 \ubabb\ud55c \uc0c1\ud0dc\uc600\uae30\uc5d0 \ucf58\ud06c\ub9ac\ud2b8 \uad6c\uc870\ubb3c\uacfc \ub85c\uceec\ub77c\uc774\uc800\ub97c \uba3c\uc800 \ube60\ub974\uac8c \ub4e4\uc774\ubc1b\uace0 \uacf5\ud56d \uc678\ubcbd \uc6b8\ud0c0\ub9ac\uc5d0 \ucd5c\uc885 \ucda9\ub3cc\ud558\uc5ec \ud3ed\ubc1c\uacfc \ud654\uc7ac\uac00 \ubc1c\uc0dd\ud574 \uaf2c\ub9ac \ubd80\ubd84\uc744 \uc81c\uc678\ud55c \uac70\uc758 \ubaa8\ub4e0 \ubd80\ubd84\uc774 \uc644\ud30c\ub418\uba70 \uc18c\uc2e4\ub418\uc5c8\ub2e4. ## \uc774 \ub54c\ubb38\uc5d0 \ud655\uc778\ub41c \uc0dd\uc874\uc790\ub294 \ubaa8\ub450 \ub3d9\uccb4\uc758 \uaf2c\ub9ac \ucabd \uc810\ud504\uc2dc\ud2b8\uc5d0\uc11c \uad6c\uc870\ub418\uc5c8\ub2e4.[\uc18c\ubc29\uccad(1\ucc28)]\uccab \ubc88\uc9f8 \ucc29\ub959 \uc2dc\ub3c4 \uc9c1\uc804\uc5d0 Flightradar24\uc5d0\uc11c \ud56d\uc801\uc774 \uc18c\uba78\ub418\uc5c8\ub2e4. \uc704 \uc0ac\uc9c4\uc744 \ubcf4\uba74 \uace0\ub3c4\uac00 \ub0ae\uc544\uc9d0\uc5d0 \ub530\ub978 ADS-B \uc218\uc2e0\uc774 \ub04a\uae30\uae30 \uc9c1\uc804\uc778 \uccab \ubc88\uc9f8 \ucc29\ub959 \uc2dc\ub3c4 \uc9c1\uc804\uae4c\uc9c0\uc758 \ud56d\uc801\uc774 \ub0a8\uaca8\uc838 \uc788\uace0, \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0 \ub2e4\ub2e4\ub974\uae30 \uc804\uc5d0 \uc57d\uac04 \uc624\ub978\ucabd\uc73c\ub85c \ube44\ud2bc \ud754\uc801\uc774 \ub0a8\uc544 \uc788\ub2e4.3.2. \uad6c\uc870 \ubc0f \uc218\uc2b5[\ud3b8\uc9d1]12\uc6d4 29\uc77c \uc624\uc804 9\uc2dc 3\ubd84 \uc0ac\uace0 \ubc1c\uc0dd \uc9c1\ud6c4, \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \uc18c\ubc29\ub300\ub294 \uc0ac\uace0\ub97c \ud655\uc778\ud558\uace0 \uace7\ubc14\ub85c \ucd9c\ub3d9\ud558\uc5ec \ud654\uc7ac\uc9c4\uc555\uc744 \uc2dc\uc791\ud588\ub2e4.\uc0ac\uace0 \uc9c1\ud6c4 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \uc18c\ubc29\ub300\uc758 \ub300\uc751  [ \ud3bc\uce58\uae30 \u00b7 \uc811\uae30 ]\uc2e0\uace0\ub97c \uc811\uc218\ubc1b\uc740 \ubb34\uc548\uc18c\ubc29\uc11c \uc120\ucc29\ub300\uac00 \ub3c4\ucc29\ud558\uace0, \uc18c\ubc29\ub2f9\uad6d\uc740"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 747a514f-3852-43a2-a65e-c31aedab068e
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 2753, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uc18c\ubc29\ub300\ub294 \uc0ac\uace0\ub97c \ud655\uc778\ud558\uace0 \uace7\ubc14\ub85c \ucd9c\ub3d9\ud558\uc5ec \ud654\uc7ac\uc9c4\uc555\uc744 \uc2dc\uc791\ud588\ub2e4.\uc0ac\uace0 \uc9c1\ud6c4 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \uc18c\ubc29\ub300\uc758 \ub300\uc751  [ \ud3bc\uce58\uae30 \u00b7 \uc811\uae30 ]\uc2e0\uace0\ub97c \uc811\uc218\ubc1b\uc740 \ubb34\uc548\uc18c\ubc29\uc11c \uc120\ucc29\ub300\uac00 \ub3c4\ucc29\ud558\uace0, \uc18c\ubc29\ub2f9\uad6d\uc740 \uc804\ub0a8\uc18c\ubc29\ubcf8\ubd80\uc7a5\uc758 \uad8c\ud55c\uc73c\ub85c \uace7\ubc14\ub85c \uc18c\ubc29 \ub300\uc751 3\ub2e8\uacc4\ub97c \ubc1c\ub839\ud558\uc600\ub2e4.[\uc18c\ubc29\uccad(1\ucc28)] \ub610\ud55c \ud654\uc7ac\uc9c4\uc555 \ubc0f \uad6c\uc870\uc791\uc5c5 \uc911 1\uba85\uc758 \uc0dd\uc874\uc790\ub97c \uad6c\uc870\ud588\ub2e4.\uc0ac\uace0 \ubc1c\uc0dd 43\ubd84 \ub4a4\uc778 \uc624\uc804 9\uc2dc 50\ubd84\uc5d0\ub294 \ucd08\uc9c4\uc774 \uc644\ub8cc#\ub418\uc5c8\uace0, \uaf2c\ub9ac\ub0a0\uac1c \ucabd \ud0d1\uc2b9\uc790\ub4e4\uc744 \uad6c\uc870 \uc911\uc774\ub2e4. \uc0ac\ub9dd\uc790\ub294 85\uba85\uc73c\ub85c \ud655\uc778\ub418\uc5c8\uc73c\uba70, \uc0dd\uc874 \uad6c\uc870\uc790\ub4e4\uc740 \ubaa9\ud3ec\ud55c\uad6d\ubcd1\uc6d0[16], \ubaa9\ud3ec\uc911\uc559\ubcd1\uc6d0\uc73c\ub85c \ud6c4\uc1a1\ub418\uace0 \uc788\ub2e4. \uc18c\ubc29 \ube44\uc0c1 \ub300\uc751\ub2e8\uacc4 3\ub2e8\uacc4\uac00 \ubc1c\ub839\ub418\uc5c8\uace0, \uc911\uc559119\uad6c\uc870\ubcf8\ubd80, \uc804\ub77c\ub0a8\ub3c4 \uc18c\ubc29\ubcf8\ubd80\uc640 \uad11\uc8fc\uad11\uc5ed\uc2dc \uc18c\ubc29\uc548\uc804\ubcf8\ubd80\ub294 \uac00\uc6a9 \uc790\uc6d0\uc744 \ud604\uc7a5\uc5d0 \ud22c\uc785\ud558\uace0 \uc788\ub2e4. 9\uc2dc 56\ubd84 \uad11\uc8fc\uacf5\ud56d\uc5d0\uc11c AW139 \uc18c\ubc29\ud5ec\uae30 HL9684\uac00 \uc774\ub959\ud574 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc73c\ub85c \uae09\ud30c\ub418\uc5c8\ub2e4.\ubd88\uae38\uc740 \uc7a1\ud614\uc9c0\ub9cc \uc0ac\uace0 \ud604\uc7a5\uc740 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \ud65c\uc8fc\ub85c \ub2f4\uc7a5 \ubc14\uae65\uae4c\uc9c0 \ud0d1\uc2b9\uc790\ub4e4\uc758 \uc2dc\uc2e0\uacfc \uc18c\uc9c0\ud488, \ud56d\uacf5\uae30 \uc794\ud574\uac00 \uc5b4\uc9c0\ub7fd\uac8c \ud769\uc5b4\uc838 \uc788\ub294 \ub9e4\uc6b0 \ucc98\ucc38\ud55c \uc0c1\ud0dc\ub77c\uace0 \ud558\uba70[17] \ud3ed\ubc1c \ud604\uc7a5\uc5d0\uc11c\ub294 \uad6c\uc870 \uc791\uc5c5, \uc2dc\uc2e0 \uc218\uc2b5\uacfc \ud568\uaed8 \uc911\uc7a5\ube44\ub97c \ub3d9\uc6d0\ud55c \ud56d\uacf5\uae30 \uc808\ub2e8 \uc791\uc5c5\uc774 \uc9c4\ud589 \uc911\uc778 \uac83\uc73c\ub85c \uc54c\ub824\uc84c\ub2e4. # \uc0ac\uace0 \ud604\uc7a5\uc5d0 \uc18c\ubc29 \ubfd0\ub9cc \uc544\ub2c8\ub77c \uad70\uacbd[18]\ub3c4 \uac19\uc774 \ud22c\uc785\ub418\uc5b4 \uad6c\uc870 \uc791\uc5c5\uc744 \uc9c0\uc6d0\ud588\ub2e4.\uad6c\uc870 \uc7a5\uba74 - MBC \ub274\uc2a4\ud2b9\ubcf412\uc2dc 30\ubd84, \uc18c\ubc29\ub2f9\uad6d\uc740 \ucd08\uae30\uc5d0 \uad6c\ucd9c\ub41c 2\uba85\uc744 \uc81c\uc678\ud55c \ubaa8\ub4e0 \ud0d1\uc2b9\uac1d\ub4e4\uc774 \uc0ac\ub9dd\ud55c \uac83\uc73c\ub85c \ucd94\uc815\ub41c\ub2e4\uace0 \ubc1c\ud45c\ud588\ub2e4. #13\uc2dc 06\ubd84, \uc18c\ubc29\ub2f9\uad6d\uc740 \uc2e4\uc885\uc790\uc758 \ub300\ubd80\ubd84\uc774 \uc0ac\ub9dd\ud55c \uac83\uc73c\ub85c \ud310\ub2e8\ud558\uace0, \uc9c0\uae08\uae4c\uc9c0 \uc9c4\ud589\ub418\uace0 \uc788\ub358 \uad6c\uc870 \uc791\uc5c5\uc744 \uc218\uc2b5 \uc791\uc5c5\uc73c\ub85c \uc804\ud658\ud55c\ub2e4\uace0 \ubc1d\ud614\ub2e4. #21\uc2dc 03\ubd84, \uc18c\ubc29\ub2f9\uad6d\uc774 \ubaa8\ub4e0 \uc2dc\uc2e0\uc744 \uc218\uc2b5\ud588\ub2e4\uace0 \ubc1d\ud614\uc73c\uba70 \ucd5c\uc885\uc801\uc73c\ub85c\ub294 \ucd5c\ucd08\uc5d0 \uad6c\uc870\ub41c 2\uba85\uc758 \uc2b9\ubb34\uc6d0\uc744 \uc81c\uc678\ud55c 179\uba85 \uc804\uc6d0\uc774 \uc0ac\ub9dd\ud55c \uac83\uc73c\ub85c \ud655\uc778\ub418\uc5c8\ub2e4.3.2.1. \ud0d1\uc2b9\uac1d \ubc0f \uad6d\uc801 \ud604\ud669[\ud3b8\uc9d1]\ud0d1\uc2b9\uac1d \uad6d\uc801\ud0d1\uc2b9\uac1d \uc218\uc0ac\ub9dd\uc790 \uc218\ube44\uace0 \ub300\ud55c\ubbfc\uad6d \ud589\uc815\uad6c \uc18d\ub839179\uba85(\uc2b9\ubb34\uc6d0 6\uba85 \ud3ec\ud568)177\uba85\uc2b9\ubb34\uc6d0 2\uba85 \ubd80\uc0c1(\uc911\uacbd\uc0c1)\uc2b9\uac1d \uc804\uc6d0 \uc0ac\ub9dd\uc2b9\ubb34\uc6d0 4\uba85 \uc0ac\ub9dd \ud0dc\uad6d \ud589\uc815\uad6c \uc18d\ub8392\uba85[19]2\uba85\uc804\uc6d0"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 1f5908c1-4ca2-4c3d-a1fd-e6ecdae9759b
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 3640, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uad6d\uc801\ud0d1\uc2b9\uac1d \uc218\uc0ac\ub9dd\uc790 \uc218\ube44\uace0 \ub300\ud55c\ubbfc\uad6d \ud589\uc815\uad6c \uc18d\ub839179\uba85(\uc2b9\ubb34\uc6d0 6\uba85 \ud3ec\ud568)177\uba85\uc2b9\ubb34\uc6d0 2\uba85 \ubd80\uc0c1(\uc911\uacbd\uc0c1)\uc2b9\uac1d \uc804\uc6d0 \uc0ac\ub9dd\uc2b9\ubb34\uc6d0 4\uba85 \uc0ac\ub9dd \ud0dc\uad6d \ud589\uc815\uad6c \uc18d\ub8392\uba85[19]2\uba85\uc804\uc6d0 \uc0ac\ub9dd\ud569\uacc4181\uba85179\uba85-3.3. \uc0ac\ub9dd\uc790 \ubd84\uc11d[\ud3b8\uc9d1]\uc131\ubcc4 \uae30\uc900\uc131\ubcc4\uc0ac\ub9dd\uc790 \uc218\ube44\uc728\ub0a8\uc13182\uba85-\uc5ec\uc13184\uba85-\ubbf8\uc0c113\uba85-\uc5f0\ub839\ub300 \uae30\uc900\uc5f0\ub839\ub300\uc0ac\ub9dd\uc790 \uc218\ube44\uc72810\uc138 \ubbf8\ub9cc5\uba85[20]2.79%10\ub3009\uba855.03%20\ub30010\uba855.59%30\ub30016\uba858.94%40\ub30032\uba8517.88%50\ub30040\uba8522.35%60\ub30039\uba8521.79%70\ub30024\uba8513.41%\ubbf8\uc0c14\uba852.23%\uc9c0\ubb38\uc73c\ub85c \uc2e0\uc6d0\ud655\uc778\uc744 \uc9c4\ud589\ud558\ub294 \uc911\uc774\uba70, \uc9c0\ubb38 \ud30c\uc545\uc774 \ubd88\uac00\ub2a5\ud55c \uc18c\uc0ac\uccb4\ub294 \uad11\uc8fc\uacfc\ud559\uc218\uc0ac\uc5f0\uad6c\uc18c\uc5d0\uc11c \uac10\uc2dd \uc911\uc774\ub2e4.\uc0ac\ub9dd\uc790 \uc911 81\uba85\uc740 \uad11\uc8fc\uad11\uc5ed\uc2dc\uc5d0, 76\uba85\uc740 \uc804\ub77c\ub0a8\ub3c4\uc5d0, 6\uba85\uc740 \uc804\ubd81\ud2b9\ubcc4\uc790\uce58\ub3c4, 2\uba85\uc740 \uc81c\uc8fc\ud2b9\ubcc4\uc790\uce58\ub3c4\uc5d0 \uc8fc\uc18c\ub97c \ub4f1\ub85d\ud55c \uc2dc\ubbfc\ub4e4\uc774\ub2e4.3.4. \ub300\uc751[\ud3b8\uc9d1]3.4.1. \ud589\uc815\ubd80[\ud3b8\uc9d1]\ucd5c\uc0c1\ubaa9 \ub300\ud1b5\ub839 \uad8c\ud55c\ub300\ud589(\uad6d\ubb34\ucd1d\ub9ac \uc9c1\ubb34\ub300\ud589, \uacbd\uc81c\ubd80\ucd1d\ub9ac \uacb8 \uae30\ud68d\uc7ac\uc815\ubd80 \uc7a5\uad00)\uc740 \ubaa8\ub4e0 \uc7a5\ube44\uc640 \uc778\ub825\uc744 \ub3d9\uc6d0\ud574 \uc778\uba85\uc744 \uad6c\uc870\ud558\ub77c\uace0 \uc9c0\uc2dc\ud55c \ud6c4, \uc0ac\uace0\ud604\uc7a5\uc744 \ubc29\ubb38\ud574 \uc0c1\ud669\uc744 \uc810\uac80\ud558\uace0 \uc720\uc871\ub4e4\uc744 \ub9cc\ub0ac\ub2e4. #\uace0\uae30\ub3d9 \ud589\uc815\uc548\uc804\ubd80 \uc7a5\uad00 \uc9c1\ubb34\ub300\ud589(\ud589\uc815\uc548\uc804\ubd80 \ucc28\uad00) \uc5ed\uc2dc \uad6c\uc870\uc5d0 \ucd1d\ub825\uc744 \ub2e4\ud558\ub77c\ub294 \uc9c0\uc2dc\ub97c \ub0b4\ub838\ub2e4. #\uc774\ud638\uc601 \uacbd\ucc30\uccad\uc7a5 \uc9c1\ubb34\ub300\ud589(\uacbd\ucc30\uccad \ucc28\uc7a5)\uc740 \uc804\ub77c\ub0a8\ub3c4 \ubb34\uc548\uad70 \uc9c0\uc5ed\uc5d0 \uac11\ud638\ube44\uc0c1\uc744, \uc778\uadfc\uc758 \ud568\ud3c9\uad70, \ubaa9\ud3ec\uc2dc \uc9c0\uc5ed\uc740 \uc744\ud638\ube44\uc0c1\uc744 \ubc1c\ub839\ud55c \ub4a4 #\uacbd\ucc30\uae30\ub3d9\ub300 \ub4f1 \uacbd\ucc30\ubcd1\ub825\uc744 \ucd1d\ub3d9\uc6d0\ud574 \uad6c\uc870\uc791\uc5c5 \uc9c0\uc6d0\uc744 \uc9c0\uc2dc\ud588\ub2e4. #\uc11c\ud574\uc9c0\ubc29\ud574\uc591\uacbd\ucc30\uccad\uc740 \ud574\uc591\uacbd\ucc30\ud2b9\uacf5\ub300\ub97c \uad6c\uc870\uc791\uc5c5\uc5d0 \ud22c\uc785\ud558\uc600\ub2e4.\uad6d\ubc29\ubd80\ub294 \ub300\ud55c\ubbfc\uad6d \uc721\uad70\uc758 \uc81c31\ubcf4\ubcd1\uc0ac\ub2e8 \uc608\ud558 \ud654\uc0dd\ubc29\ud14c\ub7ec\ud2b9\uc218\uc784\ubb34\ub300, \uc704\ud5d8\uc131 \ud3ed\ubc1c\ubb3c\uac1c\ucc99\ud300, \uc81c96\ubcf4\ubcd1\uc5ec\ub2e8 \uc81c1\ub300\ub300 \uc778\uc6d0  24\uba85, \uc721\uad70\ud2b9\uc218\uc804\uc0ac\ub839\ubd80 \uc7ac\ub09c\uc2e0\uc18d\ub300\uc751\ubd80\ub300 \uc2e0\uc18d\ub300\uc751\uc911\ub300, \uc81c11\uacf5\uc218\ud2b9\uc804\uc5ec\ub2e8 \uc81c61\ud2b9\uc804\ub300\ub300 \ub4f1 46\uba85\uc744 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0 \ud22c\uc785\ud588\ub2e4. # \uc81c1\uc804\ud22c\ube44\ud589\ub2e8\uc740 \ucd9c\ub3d9 \ub300\uae30\ub97c \ud588\ub2e4. #\ub300\ud1b5\ub839\uc2e4\uc740 \uc815\uc9c4\uc11d \ub300\ud1b5\ub839\ube44\uc11c\uc2e4\uc7a5 \uc8fc\uc7ac\ub85c \uae34\uae09\uc218\uc11d\ud68c\uc758\ub97c \uc18c\uc9d1\ud588\ub2e4. # \ub610\ud55c \ubb34\uc548\uacf5\ud56d \uc5ec\uac1d\uae30 \uc0ac\uace0 \ub300\uc751\uc744 \uc704\ud574 \"\uad6d\uc815\uc0c1\ud669\uc2e4\uc744 \uc911\uc2ec\uc73c\ub85c \ub300\ud1b5\ub839 \ube44\uc11c\uc2e4\uacfc \uad6d\uac00\uc548\ubcf4\uc2e4\uc774 24\uc2dc\uac04 \ube44\uc0c1 \ub300\uc751 \ud0dc\uc138\ub97c \uc720\uc9c0\ud558\uae30\ub85c"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 6e95d7f5-4627-4650-84c5-02d9819cbce9
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 4540, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ub300\ud1b5\ub839\ube44\uc11c\uc2e4\uc7a5 \uc8fc\uc7ac\ub85c \uae34\uae09\uc218\uc11d\ud68c\uc758\ub97c \uc18c\uc9d1\ud588\ub2e4. # \ub610\ud55c \ubb34\uc548\uacf5\ud56d \uc5ec\uac1d\uae30 \uc0ac\uace0 \ub300\uc751\uc744 \uc704\ud574 \"\uad6d\uc815\uc0c1\ud669\uc2e4\uc744 \uc911\uc2ec\uc73c\ub85c \ub300\ud1b5\ub839 \ube44\uc11c\uc2e4\uacfc \uad6d\uac00\uc548\ubcf4\uc2e4\uc774 24\uc2dc\uac04 \ube44\uc0c1 \ub300\uc751 \ud0dc\uc138\ub97c \uc720\uc9c0\ud558\uae30\ub85c \ud588\ub2e4\"\uace0 \ubc1d\ud614\ub2e4.#\uad6d\ud1a0\uad50\ud1b5\ubd80\ub294 \ud56d\uacf5\ucca0\ub3c4\uc0ac\uace0\uc870\uc0ac\uc704\uc6d0\ud68c \uc870\uc0ac\uad00\ub4e4\uc744 \uc0ac\uace0\ud604\uc7a5\uc5d0 \uae09\ud30c\ud588\ub2e4.[21] #\ud569\ub3d9\ucc38\ubaa8\ubcf8\ubd80\ub294 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0 \uad70 \ubcd1\ub825\uc744 \uae09\ud30c\ud558\uc5ec \uc0ac\uace0 \uc218\uc2b5 \uc9c0\uc6d0\uc744 \uc9c0\uc2dc\ud588\ub2e4. #\uad6d\ud1a0\uad50\ud1b5\ubd80\uac00 \uc788\ub294 \uc815\ubd80\uc138\uc885\uccad\uc0ac\uc5d0 \uc911\uc559\uc0ac\uace0\uc218\uc2b5\ub300\ucc45\ubcf8\ubd80\uac00 \uc124\uce58\ub418\uc5c8\ub2e4.\ubcf4\uac74\ubcf5\uc9c0\ubd80\ub294 \uc0ac\uace0 \uc9c0\uc5ed\uc5d0 \ucf54\ub4dc \uc624\ub80c\uc9c0\ub97c \ubc1c\ub839\ud558\uace0 \ubcf4\uac74\uc18c \uc2e0\uc18d\ub300\uc751\ubc18\uacfc \uc7ac\ub09c\uc758\ub8cc\uc9c0\uc6d0\ud300(DMAT)[22]\uc744 \uae09\ud30c\ud558\uc600\ub2e4. # \ub2e4\uc218\uc758 \uc0ac\ub9dd\uc790 \ubc1c\uc0dd\uc774 \ud655\uc778\ub418\uc790 22\uba85\uc758 \uc7a5\ub840\uc9c0\ub3c4\uc0ac\uc640 10\ub300 \uc774\uc0c1 \uc6b4\uad6c\ucc28\ub3c4 \uc9c0\uc6d0\ud558\uae30\ub85c \ud588\ub2e4. #\ud55c\uad6d\ucca0\ub3c4\uacf5\uc0ac\ub294 \uc720\uac00\uc871 \uc9c0\uc6d0\uc744 \uc704\ud55c \uc784\uc2dc\uc5f4\ucc28\ub97c \uae34\uae09 \ud3b8\uc131\ud588\ub2e4. \uc720\uac00\uc871\uc740 \ubb34\ub8cc\ub85c \ud0d1\uc2b9\uc774 \uac00\ub2a5\ud558\uba70, \ucd94\ud6c4\uc5d0\ub3c4 \uc720\uac00\uc871\uc744 \uc704\ud55c \uc784\uc2dc\uc5f4\ucc28\ub97c \uc801\uadf9 \ubc30\ucc28\ud560 \uacc4\ud68d\uc774 \uc788\ub2e4\uace0 \ubc1d\ud614\ub2e4. \ud558\ud589 \uc5f4\ucc28\ub294 \uc624\ud6c4 3\uc2dc \uc11c\uc6b8\uc5ed\uc5d0\uc11c \ucd9c\ubc1c\ud574 \uad11\uba85\uc5ed, \uc624\uc1a1\uc5ed, \uc775\uc0b0\uc5ed, \ub098\uc8fc\uc5ed\uc744 \uacbd\uc720\ud574 \ubaa9\ud3ec\uc5ed\uc73c\ub85c \uac00\uba70 \uc0c1\ud589 \uc5f4\ucc28\ub294 \uc624\ud6c4 8\uc2dc 30\ubd84 \ubaa9\ud3ec\uc5ed\uc744 \ucd9c\ubc1c\ud574 \ub098\uc8fc\uc5ed, \uc775\uc0b0\uc5ed, \uc624\uc1a1\uc5ed, \uad11\uba85\uc5ed\uc744 \uac70\uccd0 \uc11c\uc6b8\uc5ed\uc5d0 \ub3c4\ucc29\ud55c\ub2e4. #\ud55c\uad6d\uacf5\ud56d\uacf5\uc0ac\ub294 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0 \uc0ac\uace0\uc218\uc2b5\ubcf8\ubd80\ub97c \uad6c\uc131\ud558\uc600\ub2e4. #\uc0ac\uace0 \uc9c1\ud6c4 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc740 \ud3d0\uc1c4\ub418\uc5c8\uace0 \uad6d\ud1a0\uad50\ud1b5\ubd80\ub294 09\uc2dc 29\ubd84\ubd80\ub85c \ubb34\uc548\uad6d\uc81c\uacf5\ud56d(RKJB)\uc5d0 \ub300\ud558\uc5ec \ud65c\uc8fc\ub85c \ud3d0\uc1c4\ub97c \uc54c\ub9ac\ub294 NOTAM\uc744 \uac8c\uc2dc\ud558\uc600\ub2e4.[23] \uc774\ud6c4 2025\ub144 1\uc6d4 1\uc77c \uc624\uc804 5\uc2dc\uae4c\uc9c0 \ud65c\uc8fc\ub85c \ud3d0\uc1c4 \uc870\uce58\uac00 \uc5f0\uc7a5\ub410\ub2e4. \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0 \uc774\ucc29\ub959\ud558\ub294 \uc9c4\uc5d0\uc5b4 LJ227, LJ467, LJ468 \ubc0f \uc81c\uc8fc\ud56d\uacf5 7C341, 7C342\ud3b8, \uce84\ubcf4\ub514\uc544 \ud56d\uacf5 KR542\ud3b8 \ub4f1 \ubaa8\ub4e0 \ud56d\uacf5\ud3b8\uc774 \uacb0\ud56d\ub418\uc5c8\ub2e4. #\ucd5c\uc0c1\ubaa9 \uad8c\ud55c\ub300\ud589\uc774 12\uc2dc 55\ubd84\uc5d0 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \uc0ac\uace0 \ud604\uc7a5\uc5d0 \ub3c4\ucc29\ud558\uc5ec \uc0ac\uace0 \uc218\uc2b5\uc744 \uc9c0\ud718\ud558\uae30 \uc2dc\uc791\ud588\ub2e4. # #2\ucd5c\uc0c1\ubaa9 \uad8c\ud55c\ub300\ud589\uc774 14\uc2dc 20\ubd84 \uc0ac\uace0\uac00 \ub09c \ubb34\uc548\uad70 \uc804\uc5ed\uc744 \ud2b9\ubcc4\uc7ac\ub09c\uc9c0\uc5ed\uc73c\ub85c \uc120\ud3ec\ud588\ub2e4. #\ub300\uac80\ucc30\uccad\uc774 18\uc2dc 38\ubd84 \uc0ac\uace0 \ub300\ucc45\ubcf8\ubd80 \uad6c\uc131 \uc9c0\uc2dc\ub97c \uad11\uc8fc\uc9c0\ubc29\uac80\ucc30\uccad\uc5d0 \uae34\uae09 \ud558\ub2ec\ud588\uc73c\uba70, 18\uc2dc 45\ubd84 \uacf5\uc2dd \ucd9c\ubc94\ud588\ub2e4. \ubcf8\ubd80\uc7a5\uc740 \uc774\uc885\ud601 \uad11\uc8fc\uc9c0\uac80\uc7a5\uc774"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 1cf9e28c-fae7-42ad-8edd-62eb9ea53ecf
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 5441, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ud2b9\ubcc4\uc7ac\ub09c\uc9c0\uc5ed\uc73c\ub85c \uc120\ud3ec\ud588\ub2e4. #\ub300\uac80\ucc30\uccad\uc774 18\uc2dc 38\ubd84 \uc0ac\uace0 \ub300\ucc45\ubcf8\ubd80 \uad6c\uc131 \uc9c0\uc2dc\ub97c \uad11\uc8fc\uc9c0\ubc29\uac80\ucc30\uccad\uc5d0 \uae34\uae09 \ud558\ub2ec\ud588\uc73c\uba70, 18\uc2dc 45\ubd84 \uacf5\uc2dd \ucd9c\ubc94\ud588\ub2e4. \ubcf8\ubd80\uc7a5\uc740 \uc774\uc885\ud601 \uad11\uc8fc\uc9c0\uac80\uc7a5\uc774 \ub9e1\uc73c\uba70, \ud615\uc0ac3\ubd80\uc640 \uacf5\uacf5\uc218\uc0ac\ubd80, \ubaa9\ud3ec\uc9c0\uccad \ud615\uc0ac2\ubd80\uc5d0\uc11c \ucc28\ucd9c\ud574 16\uba85 \uaddc\ubaa8\ub85c \uad6c\uc131\ud588\ub2e4. \uc18c\ubc29, \uacbd\ucc30 \ub4f1 \uc720\uad00\uae30\uad00\uacfc \uae34\ubc00\ud788 \ud611\ub825\ud558\uc5ec \uc2e0\uc6d0 \ud655\uc778 \ubc0f \uac80\uc2dc \ud65c\ub3d9, \uc6d0\uc778 \uaddc\uba85, \uc720\uac00\uc871 \uc9c0\uc6d0 \ub4f1\uc744 \uc9c4\ud589\ud560 \ubc29\uce68\uc774\ub77c\uace0 \ubc1d\ud614\ub2e4. #\uc724\uc11d\uc5f4 \ub300\ud1b5\ub839[\uc9c1\ubb34\uc815\uc9c0]\uc774 18\uc2dc 38\ubd84, \ud398\uc774\uc2a4\ubd81\uc5d0 \"\ucc38\ub2f4\ud55c \uc0ac\uace0\"\ub77c\uba70 \"\uc720\uac00\uc871\ubd84\ub4e4\uaed8 \uae4a\uc740 \uc560\ub3c4\uc640 \uc704\ub85c\uc758 \ub9c8\uc74c\uc744 \uc804\ud55c\ub2e4\"\uace0 \ubc1d\ud614\ub2e4. \ub354\ubd88\uc5b4 \uc815\ubd80\uac00 \ucd5c\uc120\uc744 \ub2e4\ud560 \uac83\uc774\ub77c \ubbff\ub294\ub2e4\uba70 \uc18c\ubc29\ub300\uc6d0\ubd84\ub4e4\uc758 \uc548\uc804\ub3c4 \ucd5c\uc6b0\uc120\uc73c\ub85c \uc9c0\ucf1c\uc9c8 \uc218 \uc788\ub3c4\ub85d \ud798\uc368\ub2ec\ub77c \uac8c\uc7ac\ud588\ub2e4. # \ud398\uc774\uc2a4\ubd812025\ub144 1\uc6d4 4\uc77c 24\uc2dc\uae4c\uc9c0 \uad6d\uac00\uc560\ub3c4\uae30\uac04\uc774 \uc120\ud3ec\ub418\uc5c8\ub2e4. \ub610 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d, \uc11c\uc6b8\ud2b9\ubcc4\uc2dc, \uc804\ub77c\ub0a8\ub3c4, \uad11\uc8fc\uad11\uc5ed\uc2dc \ub4f1 \uc804\uad6d 17\uacf3\uc5d0\uc11c\ub294 \ud569\ub3d9\ubd84\ud5a5\uc18c\uac00 \uc124\uce58\ub41c\ub2e4. #3.4.2. \uc9c0\ubc29\uc790\uce58\ub2e8\uccb4[\ud3b8\uc9d1]\uae40\uc601\ub85d \uc804\ub77c\ub0a8\ub3c4\uc9c0\uc0ac\ub294 \uc989\uac01 \ud604\uc7a5\uc73c\ub85c \ud5a5\ud55c \uac83\uc73c\ub85c \uc54c\ub824\uc84c\ub2e4. \uc544\uc6b8\ub7ec \ub3c4 \ucc28\uc6d0\uc5d0\uc11c \uc7ac\ub09c\uc548\uc804\ubcf8\ubd80\ub97c \uac00\ub3d9\ud588\ub2e4. # \ud569\ub3d9\ubd84\ud5a5\uc18c \ubc0f \uc720\uac00\uc871 \ub300\uae30\uc2e4\uc744 \ubb34\uc548\uc885\ud569\uc2a4\ud3ec\uce20\ud30c\ud06c[25]\uc5d0 \uc124\uce58\ud558\uae30\ub85c \ud588\ub2e4. #\ud574\ub2f9 \uc5ec\uac1d\uae30\uc5d0\ub294 \ud654\uc21c\uad70\uc5d0\uc120 \ud604\uc9c1 3\uba85 \ubc0f \ud1f4\uc9c1 5\uba85, \ubaa9\ud3ec\uc2dc 2\uba85, \ub2f4\uc591\uad70 1\uba85, \ub179\uc0c9\uc5d0\ub108\uc9c0\uc5f0\uad6c\uc6d0 2\uba85\uc758 \uacf5\ubb34\uc6d0\uc774 \ud0d1\uc2b9\ud558\uace0 \uc788\uc5c8\ub2e4. #\uac15\uae30\uc815 \uad11\uc8fc\uad11\uc5ed\uc2dc\uc7a5\uc740 \uc624\uc804 10\uc2dc 30\ubd84\uacbd \uad11\uc8fc\uad11\uc5ed\uc2dc\uccad\uc5d0\uc11c 1\ucc28 \uc0c1\ud669 \ud30c\uc545 \ud68c\uc758\ub97c \uc8fc\uc7ac\ud558\uace0, # \uc7ac\ub09c\uc548\uc804\ub300\ucc45\ubcf8\ubd80\ub97c \uac00\ub3d9\ud588\ub2e4. #\uae40\uad00\uc601 \uc804\ubd81\ud2b9\ubcc4\uc790\uce58\ub3c4\uc9c0\uc0ac\ub294 \"\ud544\uc694\ud55c \ubaa8\ub4e0 \uc9c0\uc6d0\uc744 \ud560 \uac83\"\uc774\ub77c\uace0 \ubc1d\ud614\ub2e4. # \uc774\uc5b4 \uc804\ub77c\ub0a8\ub3c4, \uad11\uc8fc\uad11\uc5ed\uc2dc\uc640 \ud568\uaed8 \ube44\uc0c1 \ub300\uc751 \uccb4\uc81c\uc5d0 \ub3cc\uc785\ud588\ub2e4. #\uc804\ub77c\ub0a8\ub3c4\uad50\uc721\uccad\uc740 \ud0d1\uc2b9\uc790 \uc911 \uc9c1\uc6d0 5\uba85[26], \ud559\uc0dd 3\uba85[27]\uc774 \ud0d1\uc2b9\ud55c \uac83\uc744 \ud30c\uc545\ud558\uace0 \ub300\ucc45\uc744 \ub9c8\ub828\ud558\uace0 \uc788\ub2e4#\uad11\uc8fc\uad11\uc5ed\uc2dc\uad50\uc721\uccad\uc5d0\uc11c\ub3c4 \ud559\uc0dd 4\uba85\uc774 \ud0d1\uc2b9\ud55c \uac83\uc774 \ud655\uc778\ub418\uc5c8\ub2e4.\ubb34\uc548\uad70\uacfc \ub300\ud55c\uc801\uc2ed\uc790\uc0ac\uc5d0\uc11c\ub294 \uc720\uac00\uc871\ub4e4\uacfc \uc790\uc6d0\ubd09\uc0ac\uc790\ub4e4, \uadf8\ub9ac\uace0 \uc18c\ubc29\uad00\ub4e4\uc5d0\uac8c \uc2dd\uc0ac\ub97c \uc81c\uacf5\ud558\uace0 \ub2f4\uc694\ub97c \ub098\ub204\uc5b4 \uc8fc\uc5c8\uc73c\uba70 \uacf5\ud56d \uccad\uc0ac\uc5d0 \ud150\ud2b8\ub97c \uc9c0\uc5b4 \uc274 \uacf3\uc744 \ub9c8\ub828\ud574 \uc8fc\ub294 \ub4f1 \ud604\uc7a5\uc758 \uc778\uc6d0\ub4e4\uc5d0 \ub300\ud55c \uc9c0\uc6d0\uc5d0"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: d4824cda-fbce-4b11-a7a9-625dcb968c3e
encoding: FLOAT32
metadata:
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created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: f35ce82f-ed87-437b-a4cb-63dcb21ec9fa
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 7244, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ubc1c\ud45c\uac00 \uc62c\ub77c\uc654\ub2e4.[\uc804\ubb38]\uc81c\uc8fc\ud56d\uacf5 \ub300\uad6d\ubbfc \ubc1c\ud45c\uba3c\uc800 \uc81c\uc8fc\ud56d\uacf5\uc744 \uc544\uaef4\uc8fc\uc2dc\ub294 \ubaa8\ub4e0 \ubd84\ub4e4\uaed8 \uba38\ub9ac \uc219\uc5ec \uc0ac\uacfc\uc758 \ub9d0\uc500\uc744 \ub4dc\ub9bd\ub2c8\ub2e4.12\uc6d4 29\uc77c \uc624\uc804 9\uc2dc 3\ubd84\uacbd \ubc29\ucf55-\ubb34\uc548 7C2216\ud3b8\uc774 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0\uc11c \ucc29\ub959\ud558\ub358 \uc911 \ud654\uc7ac\uac00 \ubc1c\uc0dd\ud558\uc600\uc2b5\ub2c8\ub2e4.\ubb34\uc5c7\ubcf4\ub2e4 \uc0ac\uace0\ub85c \uc720\uba85\uc744 \ub2ec\ub9ac\ud558\uc2e0 \ud0d1\uc2b9\uac1d\ubd84\ub4e4\uacfc \uc720\uac00\uc871\ubd84\ub4e4\uaed8 \uae4a\uc740 \uc560\ub3c4\uc640 \uc0ac\uacfc\uc758 \ub9d0\uc500\uc744 \uc62c\ub9bd\ub2c8\ub2e4.\ud604\uc7ac\ub85c\uc11c\ub294 \uc0ac\uace0\uc758 \uc6d0\uc778\uc740 \uac00\ub2a0\ud558\uae30 \uc5b4\ub835\uace0, \uad00\ub828 \uc815\ubd80 \uae30\uad00\uc758 \uacf5\uc2dd\uc801\uc778 \uc870\uc0ac \ubc1c\ud45c\ub97c \uae30\ub2e4\ub824\uc57c \ud558\ub294 \uc0c1\ud669\uc785\ub2c8\ub2e4. \uc0ac\uace0 \uc6d0\uc778\uc744 \ubd88\ubb38\ud558\uace0 \ucd5c\uace0\uacbd\uc601\uc790\ub85c\uc11c \ucc45\uc784\uc744 \ud1b5\uac10\ud569\ub2c8\ub2e4.\uc800\ud76c \uc81c\uc8fc\ud56d\uacf5\uc740 \ube60\ub978 \uc0ac\uace0 \uc218\uc2b5\uacfc \ud0d1\uc2b9\uc790 \uac00\uc871 \uc9c0\uc6d0\uc744 \uc704\ud574 \ubaa8\ub4e0 \ub178\ub825\uc744 \ub2e4\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc544\uc6b8\ub7ec \uc815\ubd80\uc640 \ud568\uaed8 \uc0ac\uace0 \uc6d0\uc778 \uaddc\uba85\uc5d0\ub3c4 \ucd5c\uc120\uc744 \ub2e4\ud558\uaca0\uc2b5\ub2c8\ub2e4.\ub2e4\uc2dc \ud55c\ubc88 \uc0ac\uace0\ub85c \uc778\ud574 \uc720\uba85\uc744 \ub2ec\ub9ac\ud558\uc2e0 \ubd84\ub4e4\uc758 \uba85\ubcf5\uc744 \ube4c\uace0, \uc720\uac00\uc871 \uc5ec\ub7ec\ubd84\uaed8\ub3c4 \uae4a\uc740 \uc0ac\uacfc\uc758 \ub9d0\uc500\uc744 \ub4dc\ub9bd\ub2c8\ub2e4.2024\ub144 12\uc6d4 29\uc77c\uc81c\uc8fc\ud56d\uacf5 \ub300\ud45c\uc774\uc0ac \uae40\uc774\ubc3029\uc77c \uc624\ud6c4 17\uc2dc 30\ubd84 \uae30\uc900, \uc81c\uc8fc\ud56d\uacf5\uc758 \uacf5\uc2dd \uc720\ud29c\ube0c\uc640 \uc778\uc2a4\ud0c0 \uacc4\uc815, \ud398\uc774\uc2a4\ubd81 \uacc4\uc815\uc774 \ubaa8\ub450 \ube44\ud65c\uc131\ud654\ub418\uc5c8\ub2e4.29\uc77c \uc624\ud6c4 18\uc2dc 20\ubd84 \uc804\ud6c4\ub85c \uc81c\uc8fc\ud56d\uacf5\uc758 \uacf5\uc2dd \uc778\uc2a4\ud0c0\uadf8\ub7a8 \uacc4\uc815\uc774 \uc7ac\ud65c\uc131\ud654 \ub418\uc5c8\uc9c0\ub9cc \uc774\ud6c4 \ub300\uad6d\ubbfc \ubc1c\ud45c \uac8c\uc2dc\ubb3c \uc774\uc678\uc758 \ubaa8\ub4e0 \uac8c\uc2dc\ubb3c\uc774 \uc0ad\uc81c\ub418\uc5c8\ub2e4.3.4.4. \ubcf4\uc789[\ud3b8\uc9d1]\uc0ac\uace0\uae30 \uc81c\uc791\uc0ac\uc778 \ubcf4\uc789\uc740 \uc0ac\uace0 \ub2f9\uc77c \uc624\ud6c4 \uc774\ubc88 \uc0ac\uace0 \ud76c\uc0dd\uc790 \uc720\uc871\uc5d0\uac8c \uc560\ub3c4\ub97c \ud45c\ud558\uba70, \ud604\uc7ac \uc81c\uc8fc\ud56d\uacf5\uacfc \uc5f0\ub77d \uc911\uc774\ub77c\uace0 \ubc1d\ud614\ub2e4. #[\uc804\ubb382]We are in contact with Jeju Air regarding flight 2216 and stand ready to support them.We extend our deepest condolences to the families who lost loved ones, and our thoughts remain with the passengers and crew.\uc6b0\ub9ac\ub294 2216\ud3b8 \ud56d\uacf5\ud3b8\uacfc \uad00\ub828\ud558\uc5ec \uc81c\uc8fc\ud56d\uacf5\uacfc \uc5f0\ub77d\uc744 \ucde8\ud558\uace0 \uc788\uc73c\uba70, \uc774\ub4e4\uc744 \uc9c0\uc6d0\ud560 \uc900\ube44\uac00 \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\uc6b0\ub9ac\ub294 \uc0ac\ub791\ud558\ub294 \uc0ac\ub78c\uc744 \uc783\uc740 \uac00\uc871\ub4e4\uc5d0\uac8c \uae4a\uc740 \uc560\ub3c4\ub97c \ud45c\ud558\uba70, \uc6b0\ub9ac \uc2b9\uac1d\uacfc \uc2b9\ubb34\uc6d0\ub4e4\uc744 \uc78a\uc9c0 \uc54a\uaca0\uc2b5\ub2c8\ub2e4.4."}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 88f9edf6-e71e-412b-935a-5a3654534b2f
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 8138, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uc81c\uc8fc\ud56d\uacf5\uacfc \uc5f0\ub77d\uc744 \ucde8\ud558\uace0 \uc788\uc73c\uba70, \uc774\ub4e4\uc744 \uc9c0\uc6d0\ud560 \uc900\ube44\uac00 \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\uc6b0\ub9ac\ub294 \uc0ac\ub791\ud558\ub294 \uc0ac\ub78c\uc744 \uc783\uc740 \uac00\uc871\ub4e4\uc5d0\uac8c \uae4a\uc740 \uc560\ub3c4\ub97c \ud45c\ud558\uba70, \uc6b0\ub9ac \uc2b9\uac1d\uacfc \uc2b9\ubb34\uc6d0\ub4e4\uc744 \uc78a\uc9c0 \uc54a\uaca0\uc2b5\ub2c8\ub2e4.4. \uc6d0\uc778[\ud3b8\uc9d1]\uad6d\ud1a0\uad50\ud1b5\ubd80\uc758 3\ucc28 \ube0c\ub9ac\ud551[32]\uc5d0\uc11c \ub300\ub7b5\uc801\uc778 \uc0ac\uace0 \ub2f9\uc2dc \uad00\uc81c \ub0b4\uc6a9\uacfc \uc870\uc0ac \uc9c4\ucc99 \uc0c1\ud669\uc5d0 \ub300\ud55c \uc124\uba85\uc774 \uc9c4\ud589\ub418\uc5c8\ub2e4.\ud56d\uacf5\uae30\ub294 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc758 01\ubc88 \ud65c\uc8fc\ub85c, \uc989 \ub0a8\ucabd\uc5d0\uc11c \ubd81\ucabd \ubc29\ud5a5\uc73c\ub85c \uc811\uadfc \uc911\uc774\uc5c8\ub2e4. \uc0ac\uace0 \ubc1c\uc0dd 3\ubd84 \uc804 \uad00\uc81c\ud0d1\uc5d0\uc11c \uc870\ub958 \ucda9\ub3cc \uacbd\ubcf4\ub97c \ub0b4\ub838\uc73c\uba70, \uadf8\ub85c\ubd80\ud130 1\ubd84 \ub4a4 \uae30\uc7a5\uc774 \uba54\uc774\ub370\uc774\ub97c \uc120\uc5b8\ud558\uc600\ub2e4. \uad6d\ud1a0\uad50\ud1b5\ubd80\ub294 '\ucc29\ub959 \uc804 \uad00\uc81c\ud0d1\uc5d0\uc11c \uc870\ub958 \ucda9\ub3cc \uc8fc\uc758 \uacbd\uace0\ub97c \uc92c\uace0, \uc870\uc885\uc0ac\uac00 \uc9c1\ud6c4\uc5d0 \uc870\ub09c \uc2e0\ud638\ub97c \ubcf4\ub0c8\ub2e4.', '\uc774\ud6c4 \uad00\uc81c\ud0d1\uc5d0\uc11c \ubc18\ub300 \ubc29\ud5a5 \ud65c\uc8fc\ub85c[33]\ub85c \ucc29\ub959\ud558\ub3c4\ub85d \ud5c8\uac00\ub97c \uc918\uc11c \uc870\uc885\uc0ac\uac00 \uc218\uc6a9\uc744 \ud558\uace0 \ucc29\ub959\ud558\ub294 \uacfc\uc815\uc5d0\uc11c \ub2f4\ubcbc\ub77d\uacfc \ucda9\ub3cc\ud588\ub2e4.'\ub77c\uace0 \uc0ac\uac74 \uacbd\uc704\ub97c \uc124\uba85\ud588\ub2e4.[\uad6d\ud1a0\ubd80(3\ucc28)]\ud56d\uacf5\uae30 \ube14\ub799\ubc15\uc2a4\uc778 FDR[35]\uacfc CVR[36], \ub450 \uc7a5\uce58 \ubaa8\ub450 \uad6d\ud1a0\uad50\ud1b5\ubd80 \ud56d\uacf5\ucca0\ub3c4\uc0ac\uace0\uc870\uc0ac\uc704\uc6d0\ud68c\uac00 \ud655\ubcf4\ud558\uc5ec \uc218\uac70[37]\ud558\uc600\ub2e4\uace0 \ubc1d\ud614\ub2e4.[\uad6d\ud1a0\ubd80(3\ucc28)][\uad6d\ud1a0\ubd80(4\ucc28)]\ud558\uc9c0\ub9cc FDR \ub610\ud55c \uc0ac\uace0\uc758 \uc5ec\ud30c\ub85c \uc77c\ubd80 \ud6fc\uc190\uc774 \ub418\uc5b4 \ud574\ub3c5\ud558\ub294 \ub370 \uc2dc\uac04\uc774 \ub9ce\uc774 \uac78\ub9b4 \uac83\uc73c\ub85c \ubcf4\uc774\uba70 \ubbf8\uad6d NTSB\uc5d0 \ub3c4\uc6c0\uc744 \uc694\uccad\ud574\uc57c \ud560 \uc218\ub3c4 \uc788\ub2e4\ub294 \ubcf4\ub3c4\uac00 \ub098\uc654\ub2e4.#4.1. \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c \uac00\ub2a5\uc131[\ud3b8\uc9d1]\ucd9c\ucc98: MBC \ub274\uc2a4\ud2b9\ubcf4\uc5d4\uc9c4\uc5d0 \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c\uac00 \ubc1c\uc0dd\ud55c \uac83\uc73c\ub85c \ucd94\uc815\ub418\ub294 \uc774\ubbf8\uc9c0.\uc704 \uc0ac\uc9c4\uc744 \ubcf4\uba74 \uae30\uccb4\uc758 \uc6b0\uce21 \uc5d4\uc9c4 \ub4a4\ucabd\uc73c\ub85c \ubd88\uae38\uc774 \uc19f\ub294 \uac83\uc774 \ubcf4\uc778\ub2e4.\uc18c\ubc29\ub2f9\uad6d\uacfc \uad6d\ud1a0\uad50\ud1b5\ubd80\ub294 \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c(Bird strike)\ub85c \ucd94\uc815\ub418\ub294 \ubb38\uc81c\ub85c \ud56d\uacf5\uae30\uac00 \uae09\ud788 \ucc29\ub959\uc744 \uc9c4\ud589\ud588\uc73c\ub098 \ubbf8\uc0c1\uc758 \uc774\uc720[40]\ub85c \ub79c\ub529 \uae30\uc5b4\uac00 \uc804\uac1c\ub418\uc9c0 \uc54a\uc544 \uc0ac\uace0\uac00 \ubc1c\uc0dd\ud55c \uac83\uc73c\ub85c \ubcf4\uc778\ub2e4\uace0 \ub9d0\ud588\ub2e4.[41] # 500\ud53c\ud2b8\uc5d0\uc11c ADS-B \ub370\uc774\ud130 \uc2e0\ud638\uac00 \ub04a\uacbc\ub294\ub370, \ud574\ub2f9 \ud56d\uacf5\uae30\uc758 \uc774\uc804 \ube44\ud589 \uc0ac\ub840\ub098 \ub2e4\ub978 \ud56d\uacf5\uae30\uc758 \uc0ac\ub840\ub97c \uace0\ub824\ud558\uba74 ADS-B \ub370\uc774\ud130\uac00 \ub354 \uc774\uc0c1 \ud56d\uacf5\uae30\uc5d0\uc11c \uc804\uc1a1\ub418\uc9c0 \uc54a\uc740 \uac83\uc740 \ud56d\uacf5\uae30\uc5d0 \ud0d1\uc7ac\ub41c \uad00\uc81c \ubb34\uc120 \uc2dc\uc2a4\ud15c\uc758 \uc804\uc6d0 \uacf5\uae09\uc774 \ub04a\uacbc\uac70\ub098, \uad00\uc81c \ubb34\uc120 \uc2dc\uc2a4\ud15c\uc744 \ube44\ub86f\ud574 \uad11\ubc94\uc704\ud55c \uc804\uae30\uc801 \uace0\uc7a5\uc774 \ubc1c\uc0dd\ud588\uc744 \uac00\ub2a5\uc131\uc774 \uc788\ub2e4."}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: e5da990b-c1a6-4a79-b326-ab79d7040943
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 9040, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ub354 \uc774\uc0c1 \ud56d\uacf5\uae30\uc5d0\uc11c \uc804\uc1a1\ub418\uc9c0 \uc54a\uc740 \uac83\uc740 \ud56d\uacf5\uae30\uc5d0 \ud0d1\uc7ac\ub41c \uad00\uc81c \ubb34\uc120 \uc2dc\uc2a4\ud15c\uc758 \uc804\uc6d0 \uacf5\uae09\uc774 \ub04a\uacbc\uac70\ub098, \uad00\uc81c \ubb34\uc120 \uc2dc\uc2a4\ud15c\uc744 \ube44\ub86f\ud574 \uad11\ubc94\uc704\ud55c \uc804\uae30\uc801 \uace0\uc7a5\uc774 \ubc1c\uc0dd\ud588\uc744 \uac00\ub2a5\uc131\uc774 \uc788\ub2e4. \ubb3c\ub860 \uae09\ubc15\ud55c \uc0c1\ud669\uc5d0\uc11c \uc870\uc885\uc0ac\uac00 \uc2e4\uc218\ud588\uc744 \uac00\ub2a5\uc131 \ub610\ud55c \ubb34\uc2dc\ud560 \uc218 \uc5c6\ub2e4. # \uc8fc\ubbfc\ub4e4\uc758 \uc81c\ubcf4\uc5d0 \ub530\ub974\uba74 \uc0ac\uace0\uae30\ub294 \ucd94\ub77d \uc804\ubd80\ud130 \ub0ae\uac8c \ube44\ud589\ud558\uace0 \uc788\uc5c8\uc73c\uba70, \uc774\ubbf8 \uae30\uccb4\uc5d0\uc11c \uc5f0\uae30\uac00 \ub098\uc624\uace0 \uc788\uc5c8\ub2e4\uace0 \ud55c\ub2e4.MBC\uc758 \ubcf4\ub3c4\uc5d0 \ub530\ub974\uba74 \ud55c \uc804\ubb38\uac00\ub294 \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c\uc640 \ub79c\ub529\uae30\uc5b4 \ubbf8\uc791\ub3d9 \uc0ac\uc774\uc758 \uc5f0\uad00\uc131\uc774 \uac70\uc758 \uc5c6\ub2e4\uace0 \ud558\uc600\ub2e4.4.2. \ub3d9\uccb4 \ucc29\ub959(Belly Landing) \uc2e4\ud328 \uac00\ub2a5\uc131[\ud3b8\uc9d1]\uc0ac\uace0 \uc9c1\uc804 \uc218\uc9c1\uc18d\ub3c4 \uadf8\ub798\ud504(\ub178\ub780\uc0c9)\uac00 \uce58\uc19f\ub294 \ub54c\uac00 \uc788\ub294\ub370, \uc18d\ub3c4\uac00 \uc62c\ub77c\uac04 \uac83\uc774 \uc544\ub2c8\ub77c \ud68c\ud56d\uc744 \uc704\ud574\uc11c \ud558\uac15\uc744 \uba48\ucd94\ub294 \uc6c0\uc9c1\uc784\uc73c\ub85c \ubcf4\uc778\ub2e4. #, flightradar24 \uacf5\uc2dd X(\uad6c \ud2b8\uc704\ud130)\ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc758 \ud65c\uc8fc\ub85c \uae38\uc774\ub294 \uc57d 2,800m\ub85c, \uc778\ucc9c\uad6d\uc81c\uacf5\ud56d(3,750m 3\ubcf8, 4,000m 1\ubcf8)\uacfc \uae40\ud3ec\uad6d\uc81c\uacf5\ud56d(3,200m 1\ubcf8, 3,600m 1\ubcf8)\uc758 \ud65c\uc8fc\ub85c \uae38\uc774\ubcf4\ub2e4 \uc9e7\ub2e4. \uc774 \ub54c\ubb38\uc5d0 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc758 \ud65c\uc8fc\ub85c \uae38\uc774\uac00 \ube44\uc0c1\uc0c1\ud669 \uc2dc \ud56d\uacf5\uae30\uc758 \ucc29\ub959\uc5d0 \ucda9\ubd84\ud558\uc9c0 \uc54a\uc744 \uc218 \uc788\ub2e4\ub294 \uc804\ubb38\uac00\ub4e4\uc758 \uacac\ud574\uac00 \ub098\uc654\ub2e4. # \uc2e4\uc81c\ub85c \uc804\ub77c\ub0a8\ub3c4\uccad\uc758 \uc694\uccad\uc73c\ub85c \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \ud65c\uc8fc\ub85c\ub97c 3,160m\ub85c \ud655\uc7a5\ud558\ub294 \uacf5\uc0ac\uac00 \uc774\ubbf8 \uc9c4\ud589 \uc911\uc774\uc5c8\uc73c\uba70 2025\ub144 \uc644\uacf5\ub420 \uc608\uc815\uc774\uc5c8\ub2e4. \uba87\uba87 \uad50\uc218\ub4e4\uc740 \ud65c\uc8fc\ub85c\uac00 \uc5f0\uc7a5\ub418\uc5c8\ub2e4\uace0 \uc0ac\uace0\ub97c \ub9c9\uc744 \uc21c \uc5c6\uc5c8\uaca0\uc9c0\ub9cc \uc0ac\uc0c1\uc790\ub97c 1\uba85\uc774\ub77c\ub3c4 \uc904\uc774\ub294 \ub370\uc5d4 \ub3c4\uc6c0\uc774 \ub418\uc5c8\uc744 \uc218\ub3c4 \uc788\ub2e4\uace0 \ub9d0\ud588\ub2e4. \ub2e4\ub9cc \ud65c\uc8fc\ub85c\ub97c \uc774\ud0c8\ud574\uc11c \ubc1c\uc0dd\ud55c \uc0ac\uace0\uc774\uae30 \ub54c\ubb38\uc5d0 \ud65c\uc8fc\ub85c\uac00 \uae38\uc5c8\ub2e4\uace0 \uc0ac\uace0\ub85c \uc778\ud55c \ud53c\ud574\uac00 \uc904\uc9c0\ub294 \uc54a\uc558\uc744 \uac83\uc774\ub77c\ub294 \uc758\uacac\ub3c4 \uc788\ub2e4. \uc560\ucd08\uc5d0 \ud574\ub2f9 \ud65c\uc8fc\ub85c \uc5f0\uc7a5\uacf5\uc0ac \ub610\ud55c \uc0ac\uace0 \uac00\ub2a5\uc131 \ub54c\ubb38\uc774 \uc544\ub2c8\ub77c \uc911\uc7a5\uac70\ub9ac \ub178\uc120\uc744 \uc704\ud55c \ub300\ud615\uae30 \ucde8\ud56d\uc744 \ub300\ube44\ud558\ub824\ub294 \ubaa9\uc801\uc774\uc5c8\uc73c\ub098 \uc2dc\uae30\uac00 \uc808\ubb18\ud574 \ubb34\uc548\uacf5\ud56d \uc790\uccb4\uac00 \uc704\ud5d8\ud55c \uacf5\ud56d\uc774\ub77c\ub294 \uc624\ud574\ub97c \uc0ac\uae30\ub3c4 \ud588\ub2e4.# \uacb0\uad6d \uad6d\ud1a0\uad50\ud1b5\ubd80\uac00 \"\ud65c\uc8fc\ub85c \uae38\uc774 \ud0d3\uc73c\ub85c \ub09c \uc0ac\uace0\ub85c \ubcf4\uae34 \uc5b4\ub835\ub2e4\"\ub294 \uc785\uc7a5\uc744 \ubc1d\ud788\uba70 \ud65c\uc8fc\ub85c \uae38\uc774 \ub17c\ub780\uc5d0 \ub300\ud574\uc11c\ub294 \uc120\uc744 \uadf8\uc5c8\ub2e4. [\uad6d\ud1a0\ubd80(2\ucc28)][43]\ub610\ud55c \uad00\ub828 \uc790\ub8cc\uc5d0 \ub530\ub974\uba74"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 842948aa-18af-415b-8047-a0530748116c
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 9930, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uacb0\uad6d \uad6d\ud1a0\uad50\ud1b5\ubd80\uac00 \"\ud65c\uc8fc\ub85c \uae38\uc774 \ud0d3\uc73c\ub85c \ub09c \uc0ac\uace0\ub85c \ubcf4\uae34 \uc5b4\ub835\ub2e4\"\ub294 \uc785\uc7a5\uc744 \ubc1d\ud788\uba70 \ud65c\uc8fc\ub85c \uae38\uc774 \ub17c\ub780\uc5d0 \ub300\ud574\uc11c\ub294 \uc120\uc744 \uadf8\uc5c8\ub2e4. [\uad6d\ud1a0\ubd80(2\ucc28)][43]\ub610\ud55c \uad00\ub828 \uc790\ub8cc\uc5d0 \ub530\ub974\uba74 \uc0ac\uace0\uae30\uc885(B737-800)\uc758 \ucd5c\uc18c \uc694\uad6c \ud65c\uc8fc\ub85c \uae38\uc774\ub294 \ucc29\ub959\uc2dc 1,600m, \uc774\ub959\uc2dc 2,700m\ub85c, \ubb34\uc548\uacf5\ud56d\uc758 \ud604 \ud65c\uc8fc\ub85c \uae38\uc774\ub294 \ud574\ub2f9 \uae30\uc885\uc774 \uc6b4\ud56d\ud558\uae30\uc5d0 \ucda9\ubd84\ud558\uba70, \ub300\uad6c\uacf5\ud56d\uacfc \uccad\uc8fc\uacf5\ud56d \ub4f1 \uad6d\ub0b4 \uacf5\ud56d\uc774 \ubb34\uc548\uacf5\ud56d \ubcf4\ub2e4 \uc9e7\uc740 2,700m\ub300\uc758 \ud65c\uc8fc\ub85c\uc778 \uacbd\uc6b0\ub3c4 \uc788\uc73c\uba70, \uc774\ubcf4\ub2e4 \ub354 \uc9e7\uace0 \uad6d\ub0b4\uc5d0\uc11c \uac00\uc7a5 \uc9e7\uc740 \uc6b8\uc0b0\uacf5\ud56d\uc758 \ud65c\uc8fc\ub85c\ub294 2000m\uc774\ub2e4.4.3. \ub79c\ub529 \uae30\uc5b4\uc640 \uc720\uc555 \uacc4\ud1b5 \ubb38\uc81c \uac00\ub2a5\uc131[\ud3b8\uc9d1]\uc720\uacbd\uc218 \uad6d\ud1a0\uad50\ud1b5\ubd80 \ud56d\uacf5\uc548\uc804\uc815\ucc45\uad00\uc740 \"\ud1b5\uc0c1\uc801\uc73c\ub85c \uc5d4\uc9c4 \uace0\uc7a5\uacfc \ub79c\ub529\uae30\uc5b4 \uace0\uc7a5\uc740 \uc11c\ub85c \uc5f0\uacb0\ub418\uc5b4 \uace0\uc7a5 \ub098\ub294 \ubd80\ubd84\uc774 \uc5c6\uae30 \ub54c\ubb38\uc5d0 \uc5d4\uc9c4\uacfc \ub79c\ub529 \uae30\uc5b4 \ub450 \uad70\ub370\uc5d0\uc11c \ubb38\uc81c\uac00 \ubc1c\uc0dd\ud55c \uc774\ubc88 \uc0ac\uac74\uc758 \uc6d0\uc778\uc740 \uc815\ud655\ud788 \uaddc\uba85\ud560 \ud544\uc694\uac00 \uc788\ub2e4\"\uace0 \ub9d0\ud588\ub2e4.[\uad6d\ud1a0\ubd80(4\ucc28)] #\ubcf4\ud1b5 \ud55c \ucabd \uc5d4\uc9c4\uc774 \uc815\uc9c0\ud574\ub3c4 \ubc18\ub300\ucabd \uc5d4\uc9c4\uc774 \uc791\ub3d9\ud558\uba74 \uc720\uc555 \uacc4\ud1b5\uc774 \uc0b4\uc544\uc788\uc5b4 \ub79c\ub529 \uae30\uc5b4\ub97c \ub0b4\ub9b4 \uc218 \uc788\uc9c0\ub9cc, \uc0ac\uace0\uae30\ub294 \ub79c\ub529 \uae30\uc5b4\uac00 \ub0b4\ub824\uc624\uc9c0 \uc54a\uc740 \uac83\uc73c\ub85c \ubcf4\uc544 \ubaa8\ub4e0 \uc5d4\uc9c4\uc774 \uaebc\uc84c\uac70\ub098, \uc870\uc885\uc2e4 \uacc4\uae30\ud310\uc744 \ud3ec\ud568\ud55c \ubaa8\ub4e0 \uc804\uc790\uacc4\ud1b5 \uc2dc\uc2a4\ud15c\uc774 \ub2e4\uc6b4\ub418\uc5c8\uc744 \uac00\ub2a5\uc131\uc774 \ub192\ub2e4. [45] \uc911\ub825\uc744 \uc0ac\uc6a9\ud574 \uc218\ub3d9\uc73c\ub85c \ub79c\ub529 \uae30\uc5b4\ub97c \ub0b4\ub9ac\uc9c0\ub3c4 \ubabb\ud560 \uc815\ub3c4\ub85c \uc815\uc2e0\uc774 \uc5c6\uc5c8\uac70\ub098, \uc678\uad00\uc774\ub098 \uc870\uc885\uc2e4\uc5d0\uc11c \ub79c\ub529\uae30\uc5b4 \uc791\ub3d9 \uc2e0\ud638\ub97c \ud655\uc778\ud560 \uc218 \uc5c6\uc744 \uc815\ub3c4\ub85c \uc804\uc790\uacc4\ud1b5\uc774 \ub2e4\uc6b4\ub418\uc5b4\ubc84\ub9b0 \uc544\uc8fc \uc2ec\uac01\ud55c \uacc4\uae30\ud310 \ub9c8\ube44 / \uae30\uccb4 \uc190\uc0c1\uc774 \uc788\uc5c8\uc744 \uac00\ub2a5\uc131\ub3c4 \uc788\ub2e4.#4.4. \uc870\uc885\uc0ac \uacfc\uc2e4 \uac00\ub2a5\uc131[\ud3b8\uc9d1]\uad6d\ud1a0\uad50\ud1b5\ubd80\ub294 \uc0ac\uace0 \ube0c\ub9ac\ud551\uc5d0\uc11c \uae30\uc7a5\uc758 \ube44\ud589 \uc2dc\uac04\uc740 2019\ub144 3\uc6d4 \ubcf4\uc9c1\uc744 \ub9e1\uc544 6,823\uc2dc\uac04, \ubd80\uae30\uc7a5\uc758 \ube44\ud589 \uc2dc\uac04\uc740 2023\ub144 2\uc6d4\ubd80\ud130 \ubcf4\uc9c1\uc744 \ub9e1\uc544 1,650\uc2dc\uac04\uc774\ub77c\uace0 \ubc1c\ud45c\ud588\ub2e4.[46][\uad6d\ud1a0\ubd80(3\ucc28)]4.5. \uae30\ud0c0 \uacf5\ud56d\uc758 \ubb38\uc81c\uc810[\ud3b8\uc9d1]'\uacf5\ud56d\u00b7\ube44\ud589\uc7a5\uc2dc\uc124 \ubc0f \uc774\ucc29\ub959\uc7a5 \uc124\uce58\uae30\uc900' \uad6d\ud1a0\uad50\ud1b5\ubd80\uace0\uc2dc \uc81c2022-350\ud638 \uc81c1\uc7a5 \uc81c25\uc870 6\ud56d\uacfc 7\ud56d\uc5d0 \ub530\ub974\uba74 6\ud56d '\ud56d\ud589\uc5d0 \uc694\uad6c\ub418\ub294 \uc7a5\ube44\ub098 \uc2dc\uc124\uc744 \uc81c\uc678\ud55c \ubb3c\uccb4\ub85c\uc11c \ud56d\uacf5\uae30\uc5d0 \uc704\ud5d8\uc744 \uc904 \uc6b0\ub824\uac00 \uc788\ub294 \uac1c\ubc29\uad6c\uc5ed\ub0b4\uc758 \ubb3c\uccb4\ub294 \uc7a5\uc560\ubb3c\ub85c \uac04\uc8fc\ud558\uc5ec \uac00\ub2a5\ud55c \ud55c"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 81a1f3bc-b5a5-48c3-aa19-1c6c8ae73047
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 10834, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uc81c1\uc7a5 \uc81c25\uc870 6\ud56d\uacfc 7\ud56d\uc5d0 \ub530\ub974\uba74 6\ud56d '\ud56d\ud589\uc5d0 \uc694\uad6c\ub418\ub294 \uc7a5\ube44\ub098 \uc2dc\uc124\uc744 \uc81c\uc678\ud55c \ubb3c\uccb4\ub85c\uc11c \ud56d\uacf5\uae30\uc5d0 \uc704\ud5d8\uc744 \uc904 \uc6b0\ub824\uac00 \uc788\ub294 \uac1c\ubc29\uad6c\uc5ed\ub0b4\uc758 \ubb3c\uccb4\ub294 \uc7a5\uc560\ubb3c\ub85c \uac04\uc8fc\ud558\uc5ec \uac00\ub2a5\ud55c \ud55c \uc81c\uac70\ud558\uc5ec\uc57c \ud55c\ub2e4.', 7\ud56d '\uc81c6\ud56d\uc5d0\uc11c \uaddc\uc815\ud55c \ud56d\ud589\uc5d0 \uc0ac\uc6a9\ub418\ub294 \uc7a5\ube44 \ubc0f \uc2dc\uc124\uc740 \ud56d\uacf5\uae30\uc5d0 \ub300\ud55c \uc704\ud5d8\uc744 \ucd5c\uc18c\ud654 \ud560 \uc218 \uc788\ub3c4\ub85d \ubd80\ub7ec\uc9c0\uae30 \uc26c\uc6b4 \uc7ac\uc9c8\ub85c \ud558\uba70 \ucd5c\uc18c \uc911\ub7c9 \ubc0f \ub192\uc774\ub85c \uc704\uce58\ub97c \uc120\uc815\ud558\uc5ec \uc124\uce58\ud558\uc5ec\uc57c \ud55c\ub2e4.'\ub85c \uaddc\uc815\ud558\uace0 \uc788\ub2e4. # \uc544\uc6b8\ub7ec ICAO Annex14 \ucc55\ud1303 3.5.7\uc5d0 \ub530\ub974\uba74 \"\ud65c\uc8fc\ub85c \ub05d  \uc548\uc804 \uad6c\uc5ed\uc5d0 \uc704\uce58\ud55c \ud56d\uacf5\uae30\uc5d0 \uc704\ud5d8\uc744 \ucd08\ub798\ud560\uc218\uc788\ub294 \ubb3c\uccb4\ub294 \uc7a5\uc560\ubb3c\ub85c \uac04\uc8fc\ub418\uc5b4\uc57c \ud558\uba70, \uac00\ub2a5\ud55c \uc81c\uac70\ud574\uc57c \ud55c\ub2e4\"\ub85c \uaddc\uc815\ud558\uace0 \uc788\ub2e4. \ubb34\uc548\uacf5\ud56d\uc758 \uacbd\uc6b0 \ud65c\uc8fc\ub85c \ub05d\ub2e8\uc5d0 \ucf58\ud06c\ub9ac\ud2b8\ub85c \ud0c0\uc124\ud55c \uad6c\uc870\ubb3c\uc5d0 \ub85c\uceec\ub77c\uc774\uc800 \uc2dc\uc124\ubb3c\uc744 \uc62c\ub824\ub193\uc558\ub2e4. \uc774 \ub192\uc774\ub294 \ucd9c\ub3d9\ud588\ub358 \uc2a4\ud0c0\ub9ac\uc544 \uad6c\uae09\ucc28\uc758 \uc804\uace0\ubcf4\ub2e4\ub3c4 \ud6e8\uc52c \ub192\uc740 2m ~ 3m \ub300\uc758 \ub192\uc774\ub85c \ube44\ud589\uae30 \ub3d9\uccb4\uc640 \ub300\ub4f1\ud55c \ub192\uc774\uac00 \ub418\uc5b4\ubc84\ub9ac\ub294\ub370 \uc774 \uc7a5\uc560\ubb3c\uc744 \uc815\uba74\uc73c\ub85c \ucda9\ub3cc\ud558\uba74\uc11c \ucc38\uc0ac \ud53c\ud574\ub97c \ud06c\uac8c \ub9cc\ub4e4\uc5c8\ub2e4.# \ubb34\uc548\uacf5\ud56d\uc740 \uc815\uc9c0\ub85c\uc5d0\uc11c \ub85c\uceec\ub77c\uc774\uc800\uae4c\uc9c0\uc758 \uac70\ub9ac\ub294 323m\ub85c[\uad6d\ud1a0\ubd80(4\ucc28)], \ub85c\uceec\ub77c\uc774\uc800\uc758 \uc704\uce58\ub294 \uad6d\ub0b4\ubc95 \ubc0f ICAO \ubc95\uc5d0 \uc758\ud55c \uac1c\ubc29\uad6c\uc5ed(\uc548\uc804\uad6c\uc5ed)\uc758 \uae30\uc900\uc744 \ub9cc\uc871\ud55c\ub2e4. \uac1c\ubc29\uad6c\uc5ed \ub0b4 \uc7a5\uc560\ubb3c\uc758 \uc81c\uac70\ub294 \uad8c\uace0\uc0ac\ud56d\uc73c\ub85c \ucd5c\ub300\ud55c\uc758 \uc81c\uac70\ub97c \ubaa9\uc801\uc73c\ub85c \ud558\uace0 \uc788\ub2e4. \ud574\ub2f9 \uc0ac\uace0\uae30\uac00 \uc608\uc0c1 \uc774\uc0c1\uc73c\ub85c \uac10\uc18d\uc774 \ubd88\uac00\ud558\uc5ec \ub85c\uceec\ub77c\uc774\uc800\uc5d0 \ubd80\ub52a\ud614\uc744 \ubfd0, \ud574\ub2f9 \uacf5\ud56d\uc758 \uad6c\uc870\uac00 \ubc95\uc801\uc778 \ubb38\uc81c\ub97c \uc9c0\ub2c8\uace0 \uc788\ub2e4\uace0 \ubcf4\uae34 \uc5b4\ub835\ub2e4. 5. \ubc18\uc751[\ud3b8\uc9d1] \u00a0 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 \uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0/\ubc18\uc751 \ubb38\uc11c\ub97c\uc758 \ubc88 \ubb38\ub2e8\uc744\uc758  \ubd80\ubd84\uc744 \ucc38\uace0\ud558\uc2ed\uc2dc\uc624.6. \uae30\ud0c0[\ud3b8\uc9d1]\uc81c\uc8fc\ud56d\uacf5 \ucc3d\ub9bd 19\ub144 \uc5ed\uc0ac\uc0c1 \ucd5c\ucd08\ub85c \ubc1c\uc0dd\ud55c \uc778\uba85 \uc0ac\uace0\ub85c, \ucc98\uc74c\uc73c\ub85c \ubc1c\uc0dd\ud55c \uc778\uba85 \uc0ac\uace0\uac00 \ub300\ud615 \uc778\uba85\ud53c\ud574\ub85c \uc774\uc5b4\uc9c0\uac8c \ub418\uc5c8\ub2e4. \uac8c\ub2e4\uac00 \uc81c\uc8fc\ud56d\uacf5\uc758 \ubb34\uc548 ~ \ubc29\ucf55 \ub178\uc120\uc740 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \uac1c\ud56d \uc774\ud6c4 \ucd5c\ucd08\ub85c \ucde8\ud56d\ud55c \uc815\uae30 \uad6d\uc81c\uc120 \ub178\uc120 \uc911 \ud558\ub098\ub85c, 12\uc6d4 8\uc77c \ucde8\ud56d\ud558\uc5ec \ub178\uc120\uc744 \uc6b4\ud56d\ud55c\uc9c0 \ub2e8 3\uc8fc \ubc16\uc5d0 \uc548 \ub41c \ub178\uc120\uc774\uc5c8\ub2e4.\ubc1c\uc0dd \uc704\uce58\uac00 \ub300\ud55c\ubbfc\uad6d\uc774\uac70\ub098 \ub300\ud55c\ubbfc\uad6d \uad6d\uc801\uae30\uc5d0\uc11c 100\uba85 \uc774\uc0c1\uc758 \uc0ac\ub9dd\uc790\uac00 \ubc1c\uc0dd\ud55c \uc5ec\uac1d\uae30 \uc0ac\uace0\ub85c\ub294 5\ubc88\uc9f8\ub2e4."}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: feae56bb-f07f-4c6f-bf9f-f99575b2527a
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 11736, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "12\uc6d4 8\uc77c \ucde8\ud56d\ud558\uc5ec \ub178\uc120\uc744 \uc6b4\ud56d\ud55c\uc9c0 \ub2e8 3\uc8fc \ubc16\uc5d0 \uc548 \ub41c \ub178\uc120\uc774\uc5c8\ub2e4.\ubc1c\uc0dd \uc704\uce58\uac00 \ub300\ud55c\ubbfc\uad6d\uc774\uac70\ub098 \ub300\ud55c\ubbfc\uad6d \uad6d\uc801\uae30\uc5d0\uc11c 100\uba85 \uc774\uc0c1\uc758 \uc0ac\ub9dd\uc790\uac00 \ubc1c\uc0dd\ud55c \uc5ec\uac1d\uae30 \uc0ac\uace0\ub85c\ub294 5\ubc88\uc9f8\ub2e4. \uc774\ub807\uac8c \ub300\ud615 \ud56d\uacf5 \uc0ac\uace0\uac00 \ud130\uc9c4 \uc9c0 \uc2dc\uac04\uc774 \uc624\ub798 \uc9c0\ub09c \ub9cc\ud07c \uc774\ubc88 \uc0ac\uac74\uc740 \uad6d\ubbfc\ub4e4\uc5d0\uac8c \ud06c\uac8c \ucda9\uaca9\uc73c\ub85c \ub2e4\uac00\uc654\ub2e4.2002\ub144\uc5d0 \ubc1c\uc0dd\ud55c \uc911\uad6d \uad6d\uc801\uae30\uc758 \ucd94\ub77d \uc0ac\uace0(\uc911\uad6d\uad6d\uc81c\ud56d\uacf5 129\ud3b8 \ucd94\ub77d \uc0ac\uace0)\ub85c\ubd80\ud130 22\ub144 \ub9cc\uc5d0 \ub300\ud55c\ubbfc\uad6d\uc5d0\uc11c \uc5ec\uac1d\uae30 \uc0ac\ub9dd \uc0ac\uace0\uac00 \ubc1c\uc0dd\ud588\ub2e4.\ub300\ud55c\ubbfc\uad6d \uad6d\uc801 \uc5ec\uac1d\uae30\uc5d0\uc11c \uc0ac\ub9dd\uc790\uac00 \ub09c \uc0ac\uace0 \uc0ac\ub840\ub85c\ub294 1978\ub144 \ub300\ud55c\ud56d\uacf5 902\ud3b8 \uaca9\ucd94 \uc0ac\uac74, 1980\ub144 \ub300\ud55c\ud56d\uacf5 015\ud3b8 \ucc29\ub959 \uc0ac\uace0, 1983\ub144 \ub300\ud55c\ud56d\uacf5 007\ud3b8 \uaca9\ucd94 \uc0ac\uac74, 1987\ub144 \ub300\ud55c\ud56d\uacf5 858\ud3b8 \ud3ed\ud30c \uc0ac\uac74, 1989\ub144 \ub300\ud55c\ud56d\uacf5 803\ud3b8 \ucd94\ub77d \uc0ac\uace0, 1993\ub144 \uc544\uc2dc\uc544\ub098\ud56d\uacf5 733\ud3b8 \ucd94\ub77d \uc0ac\uace0, 1997\ub144 \ub300\ud55c\ud56d\uacf5 801\ud3b8 \ucd94\ub77d \uc0ac\uace0, 2013\ub144 \uc544\uc2dc\uc544\ub098\ud56d\uacf5 214\ud3b8 \ucc29\ub959 \uc0ac\uace0, 2024\ub144 \uc5d0\uc5b4\ud504\ub808\ubbf8\uc544 132\ud3b8 \ud68c\ud56d \uc0ac\uace0\uc5d0 \ub4a4\uc774\uc5b4 \ubc1c\uc0dd\ud55c \uc0ac\uace0\uc774\ub2e4.\ub300\ud55c\ubbfc\uad6d \uad6d\uc801 \uc5ec\uac1d\uae30\uac00 \ub300\ud55c\ubbfc\uad6d\uc5d0\uc11c \ucd94\ub77d\ud558\uc5ec \uc0ac\ub9dd\uc790\ub97c \ub0b8 \uc0ac\ub840\ub85c\uc368\ub294 1980\ub144 \ub300\ud55c\ud56d\uacf5 015\ud3b8 \ucc29\ub959 \uc0ac\uace0, 1993\ub144 \uc544\uc2dc\uc544\ub098\ud56d\uacf5 733\ud3b8 \ucd94\ub77d \uc0ac\uace0 \uc774\ud6c4 3\ubc88\uc9f8\uc774\ub2e4.[49]2022\ub144\uc5d0 \uc77c\uc5b4\ub09c \ub300\ud55c\ud56d\uacf5 631\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 \uc774\ud6c4 2\ub144\ub9cc\uc5d0 \ub300\ud55c\ubbfc\uad6d \uad6d\uc801\uc758 \uc5ec\uac1d\uae30\uac00 \ucc29\ub959 \uacfc\uc815\uc5d0\uc11c \ubc1c\uc0dd\ud55c \uc0ac\uace0\ub2e4.2013\ub144 \uc544\uc2dc\uc544\ub098\ud56d\uacf5 214\ud3b8 \ucc29\ub959 \uc0ac\uace0 \uc774\ud6c4 11\ub144\ub9cc\uc5d0 \uc0ac\ub9dd\uc790\uac00 \uc678\ubd80\uc801\uc778 \uc694\uc778\uc73c\ub85c \uc778\ud574 \ubc1c\uc0dd\ud55c \ub300\ud55c\ubbfc\uad6d \uad6d\uc801\uae30\uc758 \uc0ac\uace0\uc774\ub2e4.\uba87\uba87 \uc5b8\ub860\uc740 \uc800\ube44\uc6a9 \ud56d\uacf5\uc0ac\ub294 \uc704\ud5d8\ud558\ub2e4\uac70\ub098, \ubcf4\uc789\uc774 \ub9cc\ub4e0 \ube44\ud589\uae30\ub294 \ubbff\uc744 \uc218 \uc5c6\ub2e4\ub294 \uc2dd\uc73c\ub85c \ubab0\uc544\uac00\uac70\ub098[50][51] \ub2e4\ub978 \uc9c0\uc5ed\uc758 \uc0ac\uace0\ub97c \uac00\uc838\uc624\ub294 \ub4f1 \ubd88\uc548\uc744 \uc870\uc7a5\ud558\uace0 \uc788\ub2e4.\ubb34\uc548 \ucd94\ub77d\uc0ac\uace0 \ud56d\uacf5\uae30 \uae30\uc885\uc740\u20268000m \ucd94\ub77d \uc804\ub825 \uc788\uc5c8\ub2e4\"\uc0c8\ub5bc \ucda9\ub3cc\ub54c\ubb38?\"\u2026\uc804\ubb38\uac00\uac00 \ucd94\uc815\ud558\ub294 \uc0ac\uace0 \uc6d0\uc778\uc740\uacf5\uc911\uc11c \ub0a8\uc740 \uc5f0\ub8cc \uc65c \uc548 \ubc84\ub838\ub098? \"\uc0ac\uace0 \uae30\uc885\uc5d4 \uadf8 \uae30\ub2a5 \uc5c6\ub2e4\"\uc911\uad6d\uc11c \ucd94\ub77d, \ubbf8\uad6d\uc120 \ud68c\ud56d... '\uc0ac\uace0 \uc18d\ucd9c' \ub17c\ub780\uc758 \ubcf4\uc789 737-800MBC \ub274\uc2a4\ud2b9\ubcf4\uc5d0 \uc81c\ubcf4\ub41c \ubaa9\uaca9\uc790\uc758 \ub179\ud654 \uc601\uc0c1\uc5d0 \ub530\ub974\uba74 \ube44\ud589 \uc911\uc774\ub358 \ud56d\uacf5\uae30\uc758 \uc624\ub978\ucabd"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: ccbe7e5f-be69-4fa7-b969-9b890c7fa51c
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 12635, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uae30\uc885\uc5d4 \uadf8 \uae30\ub2a5 \uc5c6\ub2e4\"\uc911\uad6d\uc11c \ucd94\ub77d, \ubbf8\uad6d\uc120 \ud68c\ud56d... '\uc0ac\uace0 \uc18d\ucd9c' \ub17c\ub780\uc758 \ubcf4\uc789 737-800MBC \ub274\uc2a4\ud2b9\ubcf4\uc5d0 \uc81c\ubcf4\ub41c \ubaa9\uaca9\uc790\uc758 \ub179\ud654 \uc601\uc0c1\uc5d0 \ub530\ub974\uba74 \ube44\ud589 \uc911\uc774\ub358 \ud56d\uacf5\uae30\uc758 \uc624\ub978\ucabd \ub0a0\uac1c\uc640 \uc5d4\uc9c4\uc5d0 \uc9e7\uac8c \ubd88\uae38\uc774 \uc77c\uc5b4\ub098\ub294 \ubaa8\uc2b5\uc774 \uadf8\ub300\ub85c \uce74\uba54\ub77c\uc5d0 \ucc0d\ud614\ub2e4. \ubaa9\uaca9\uc790\uc5d0 \ub530\ub974\uba74 \ud56d\uacf5\uae30\uac00 \ub9c8\uc744\uc744 \ud53c\ud574 \ucc29\ub959\ud588\uc73c\uba70, \ube44\ud589\uae30\uac00 \uc18d\ub825\uc744 \uc904\uc774\uc9c0 \ubabb\ud588\uace0 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \ub05d\uc5d0\uc11c \uae09\ud558\uac8c \ubc29\ud5a5\uc744 \uaebe\uc740 \uac83\uc73c\ub85c \ubcf4\uc600\ub2e4\uace0 \uc99d\uc5b8\ud588\ub2e4.\ud55c\ub355\uc218 \uad6d\ubb34\ucd1d\ub9ac \ud0c4\ud575\uc18c\ucd94\ub85c \uc778\ud574 \ucd5c\uc0c1\ubaa9 \uacbd\uc81c\ubd80\ucd1d\ub9ac \uacb8 \uae30\ud68d\uc7ac\uc815\ubd80\uc7a5\uad00\uc774 \ub300\ud1b5\ub839 \uad8c\ud55c\ub300\ud589\uc744 \uc774\uc591\ubc1b\uc740\uc9c0 \ubd88\uacfc \uc774\ud2c0\ub9cc\uc5d0 \ubc1c\uc0dd\ud55c \uccab \ub300\ud615\ucc38\uc0ac\ub2e4. \ub2e4\ud589\ud788 \uc7ac\ub09c\uc758\ub8cc\uc9c0\uc6d0\ud300 \ud22c\uc785, \ud2b9\ubcc4\uc7ac\ub09c\uc9c0\uc5ed\uc120\ud3ec \ub4f1 \ub300\ud55c\ubbfc\uad6d \uc815\ubd80\uc758 \uc778\ub825\ub4e4\uc774 \ube60\ub974\uac8c \uc0ac\uace0 \ud604\uc7a5\uc5d0 \ud30c\uacac\ub418\uba74\uc11c \uc815\ubd80 \ucc28\uc6d0\uc5d0\uc11c\uc758 \ucd1d\ub825 \ub300\uc751\uc5d0 \ub098\uc11c\uace0 \uc788\ub2e4.\uc0ac\uace0 \uc9c1\ud6c4 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \ucd9c\ub3c4\ucc29 \ubaa8\ub4e0 \ud56d\uacf5\ud3b8\uc774 \uacb0\ud56d\ub418\uc5c8\uc73c\uba70, \ubb34\uc548\uad6d\uc81c\uacf5\ud56d \ud648\ud398\uc774\uc9c0\uc5d0 \uc811\uc18d\ub7c9\uc774 \ud3ed\uc8fc\ud558\uc5ec \uc0ac\uc774\ud2b8 \uc5f0\uacb0\uc774 \uc9c0\uc5f0\ub418\uae30\ub3c4 \ud588\ub2e4.\uc0ac\uace0 \ubc1c\uc0dd \uc9c1\ud6c4 Flightradar24\uc5d0\uc11c \ud5ec\ub9ac\ucf65\ud130 2\ub300\uac00 \ucd9c\ub3d9\ud55c \uac83\uc774 \ud655\uc778\ub418\uc5c8\ub2e4.\uc0ac\uace0\uac00 \ubc1c\uc0dd\ud558\uae30 \uc9c1\uc804 \ud56d\uacf5\uae30\uac00 \ucc29\ub959\ubcf5\ud589\uc744 \ud558\ub294 \ub4f1 \uc57d\uac04\uc758 \uc2dc\uac04\uc774 \uc788\uc5b4, \ud0d1\uc2b9\uc790\uac00 \uac00\uc871\ub4e4\uacfc \uce74\uce74\uc624\ud1a1\uc744 \uc8fc\uace0 \ubc1b\uae30\ub3c4 \ud588\ub2e4. #\uc815\uc0c1\ucc29\ub959\uc5d0 \uc2e4\ud328\ud588\uace0 \uae30\uccb4\ub3c4 \uc18c\uc2e4\ud588\uc9c0\ub9cc \uc81c\uc8fc\ud56d\uacf5 \ud648\ud398\uc774\uc9c0\uc5d0\ub294 \ud56d\uacf5\uae30\uac00 \uc624\uc804 8\uc2dc 50\ubd84\uc5d0 \ub3c4\ucc29\ud588\ub2e4\uace0 \ud45c\uc2dc\ud558\uace0 \uc788\uc5c8\ub2e4.\uad6c\uae00\uc5d0 \ud574\ub2f9 \ud56d\uacf5\ud3b8\uc744 \uac80\uc0c9 \uc2dc \uc9c0\uc5f0\ub3c4\ucc29\uc73c\ub85c \ud45c\uc2dc\ub41c\ub2e4. \ub2e4\ub9cc \uad6c\uae00 \ud56d\uacf5\ud3b8 \uac80\uc0c9\uc740 FlightAware \ub4f1\uc5d0\uc11c \ub370\uc774\ud130\ub97c \ubc1b\uc544\uc624\ub294 \ubc29\uc2dd\uc73c\ub85c, \uc774\uc804\uc758 \ucc29\ub959 \uc2e4\ud328\ub098 \ubd88\uc2dc\ucc29 \uc0ac\uace0 \ub4f1\uc5d0\uc11c\ub3c4 \uc9c0\uc5f0\ub3c4\ucc29\uc73c\ub85c \ud45c\uc2dc\ud55c \uc0ac\ub840\uac00 \ub9ce\uc558\ub2e4.\uc774 \uc0ac\uace0\ub85c \uc774\ub0a0 \uc608\uc815\ub41c \uc2a4\ud3ec\uce20 \uacbd\uae30\ub4e4[52]\uc740 \ucd94\ubaa8 \ucc28\uc6d0\uc5d0\uc11c \uc751\uc6d0\ub2e8 \uc6b4\uc601 \ubc0f \uc774\ubca4\ud2b8\uac00 \uc784\uc2dc \uc911\ub2e8\ub420 \uac83\uc73c\ub85c \uc608\uc0c1\ub41c\ub2e4.\ub300\ud55c\ubbfc\uad6d \uc18c\ubc29\uccad \ube0c\ub9ac\ud551\uc5d0\uc11c \ud55c \uae30\uc790\uac00 \uc0dd\uc874\ud55c \uc2b9\ubb34\uc6d0\uc758 \uc0dd\ub144\uc6d4\uc77c\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \ubb3b\ub294 \ucd94\ud0dc\ub97c \ubd80\ub838\ub2e4.\uc0ac\uace0\uac00 \uc77c\uc5b4\ub098\uae30 1\ub144 \uc804\uc778 2023\ub144 \ubb34\uc548\uad6d\uc81c\uacf5\ud56d\uc5d0\uc11c\ub294 \uacf5\uad50\ub86d\uac8c\ub3c4 \uac70\uc758 \uac19\uc740 \ub0b4\uc6a9\uc758 \uc0ac\uace0\ub97c \uc0c1\uc815\ud55c \ud6c8\ub828\uc744 \ud55c \uc801\uc774 \uc788\uc5c8\ub2e4. # \uae30\uc885\ubd80\ud130 \ud65c\uc8fc\ub85c \uc774\ud0c8 \ud6c4 \uc678\ubcbd \ucda9\ub3cc, \ud654\uc7ac \ub4f1 \uaf64 \ube44\uc2b7\ud558\ub2e4.\uc544\uc81c\ub974\ubc14\uc774\uc794 \ud56d\uacf5 8243\ud3b8 \ucd94\ub77d \uc0ac\uac74\uc73c\ub85c\ubd80\ud130 \ub098\ud758\ub9cc\uc5d0 \ubc1c\uc0dd\ud55c"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 17705861-c535-4247-8883-650cf15fa9f3
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 13536, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\uac19\uc740 \ub0b4\uc6a9\uc758 \uc0ac\uace0\ub97c \uc0c1\uc815\ud55c \ud6c8\ub828\uc744 \ud55c \uc801\uc774 \uc788\uc5c8\ub2e4. # \uae30\uc885\ubd80\ud130 \ud65c\uc8fc\ub85c \uc774\ud0c8 \ud6c4 \uc678\ubcbd \ucda9\ub3cc, \ud654\uc7ac \ub4f1 \uaf64 \ube44\uc2b7\ud558\ub2e4.\uc544\uc81c\ub974\ubc14\uc774\uc794 \ud56d\uacf5 8243\ud3b8 \ucd94\ub77d \uc0ac\uac74\uc73c\ub85c\ubd80\ud130 \ub098\ud758\ub9cc\uc5d0 \ubc1c\uc0dd\ud55c \ud56d\uacf5 \uc0ac\uace0\ub2e4.2024\ub144 1\uc6d4 2\uc77c \uc77c\ubcf8\uc5d0\uc11c \ubc1c\uc0dd\ud55c \ub3c4\ucfc4 \uad6d\uc81c\uacf5\ud56d \uc9c0\uc0c1 \ucda9\ub3cc \uc0ac\uace0\ub97c \uac10\uc548\ud558\uba74, 2024\ub144\uc740 \ud56d\uacf5\uc0ac\uace0\ub85c \uc2dc\uc791\ud574 \ud56d\uacf5\uc0ac\uace0\ub85c \ub05d\ub098\uac8c \ub41c,[53] 1985\ub144 \ubc0f 2014\ub144\uacfc \ub354\ubd88\uc5b4 \ud56d\uacf5 \uc5ed\uc0ac\uc0c1 \ube44\uadf9\uc801\uc778 \ud574 \uc911 \ud558\ub098\uac00 \ub41c \uc148\uc774\ub2e4.\uc0ac\uace0 \ubc1c\uc0dd 3\uc2dc\uac04 \uc804 \ub178\ub974\uc6e8\uc774 \uc624\uc2ac\ub85c\uc5d0\uc11c\ub3c4 \ub3d9\uc77c\ud55c 737-800 \uae30\uc885\uc758 KLM 1204\ud3b8\uc774 \ucc29\ub959 \uc911 \ud65c\uc8fc\ub85c\ub97c \uc774\ud0c8\ud558\ub294 \uc0ac\uace0\uac00 \ubc1c\uc0dd\ud588\ub2e4. \uc774 \uc0ac\uace0\ub294 \uc720\uc555 \uacc4\ud1b5 \uc190\uc2e4\uc774 \uc6d0\uc778\uc73c\ub85c \ubcf4\uc778\ub2e4. # \ub610\ud55c \uc774 \uc0ac\uace0\uac00 \uc77c\uc5b4\ub09c \uc9c0 \uba87 \uc2dc\uac04\ub3c4 \uc548 \ub418\uc5b4 \uce90\ub098\ub2e4 \ud57c\ub9ac\ud329\uc2a4\uc5d0\uc11c Q400 \uae30\uc885\uc758 PAL \ud56d\uacf5 \uc5d0\uc5b4\uce90\ub098\ub2e4 2259\ud3b8\uc774 \ucc29\ub959 \uacfc\uc815 \uc911 \uc7a5\uce58\uc5d0 \ubb38\uc81c\uac00 \uc0dd\uaca8 \ub3d9\uccb4\ucc29\ub959\ud558\ub294 \uc0ac\uace0\uac00 \ubc1c\uc0dd\ud588\ub2e4. #\uacf5\uad50\ub86d\uac8c\ub3c4 10\uac1c\uc6d4 \uc804, \uc81c\uc8fc\ud56d\uacf5 \ube14\ub77c\uc778\ub4dc\uc5d0 \"\uc81c\uc8fc\ud56d\uacf5 \ud0c0\uc9c0 \ub9c8\ub77c\", \"\uc694\uc998 \ud22d\ud558\uba74 \uc5d4\uc9c4 \uacb0\ud568\uc774\ub2e4. \uc5b8\uc81c \ub5a8\uc5b4\uc9c8\uc9c0 \ubaa8\ub978\ub2e4.\"\ub77c\ub294 \uae00#\uc774 \uc62c\ub77c\uc628 \uc801 \uc788\uc5b4\uc11c \uc774 \uc0ac\uac74 \uc774\ud6c4 \uc7ac\uc870\uba85\ub418\uc5c8\ub2e4. \uae30\uc0ac\uc720\uac00\uc871\ub4e4\uc774 \uc218\uc2b5 \uc0c1\ud669\uc744 \uc54c\ub824\uc904 \uc815\ubd80 \uad00\uacc4\uc790\ub97c \uc694\uccad\ud588\uc9c0\ub9cc \uba87 \uc2dc\uac04 \ub3d9\uc548 \ub300\uc751\uc774 \uc5c6\uace0, \ub9e4\uc2dc\uac04 \uc0c1\ud669\uc744 \uc54c\ub9ac\ub294 \ube0c\ub9ac\ud551\uc744 \ud558\uaca0\ub2e4\ub294 \uc57d\uc18d\ub3c4 \uc9c0\ud0a4\uc9c0 \uc54a\uace0 \uc788\ub2e4\uba70 \uc815\ubd80 \ub300\uc751\uc5d0 \ubd84\ud1b5\uc744 \ud130\ub728\ub838\ub2e4. \uc81c\uc8fc\ud56d\uacf5\uc774 \ube0c\ub9ac\ud551\uc744 \ud604\uc7a5\uc774 \uc544\ub2cc \uc11c\uc6b8\uc5d0\uc11c \ud558\ub294 \uac83\uc5d0 \ub300\ud574\uc11c\ub3c4 \ubd84\ud1b5\uc774 \ud130\uc838\ub098\uc654\ub294\ub370 \ud55c \uc720\uac00\uc871\uc740 \"\uc774 \ub113\uc740 \uacf5\ud56d\uc5d0 \uc81c\uc8fc\ud56d\uacf5 \uad00\uacc4\uc790\ub294 \ud558\ub098\ub3c4 \ubcf4\uc774\uc9c0 \uc54a\ub294\ub2e4.\"\uace0 \ubd84\uac1c\ud588\ub2e4. #\uc0ac\uace0\ub85c \uc778\ud55c \ud63c\ub780\uc744 \ud2c8\ud0c0 \ucd9c\ucc98\ub97c \uc54c \uc218 \uc5c6\ub294 \uc815\ubcf4\uc640 \uc74c\ubaa8\ub860 \ub4f1\uc774 \uc720\ud29c\ube0c \ub4f1\uc744 \ud1b5\ud574 \uc720\ud3ec\ub418\uace0 \uc788\ub2e4.#\uc0ac\uace0 \ud6c4 \uad6c\uc870\ub418\uc5b4 \ubaa9\ud3ec\ud55c\uad6d\ubcd1\uc6d0\uc73c\ub85c \ud6c4\uc1a1\ub41c \uc2b9\ubb34\uc6d0\uc740 \"\uc5b4\ub514\uac00 \uc544\ud504\ub0d0\"\ub294 \uc758\uc0ac\uc758 \uc9c8\ubb38\uc5d0 \"\uc5b4\ub5bb\uac8c \ub41c \uc77c\uc774\ub0d0\"\uba70 \"\ub0b4\uac00 \uc5ec\uae30\uc5d0 \uc65c \uc624\uac8c \ub41c \uac83\uc774\ub0d0\"\uace0 \ub418\ubb3c\uc5c8\ub2e4\uace0 \ud55c\ub2e4. \uc2b9\ubb34\uc6d0\uc740 \"\ucc29\ub959\uc744 \uc55e\ub450\uace0 \uc548\uc804\ubca8\ud2b8\ub97c \ub9e4\uace0 \uc788\uc5c8\uace0 \ube44\ud589\uae30\uac00 \ub2e4 \ucc29\ub959\ud55c \uac83 \uac19\uc558\ub294\ub370 \uadf8 \uc774\ud6c4\ub294 \uae30\uc5b5\uc774 \uc5c6\ub2e4\"\uace0\ub3c4 \ub9d0\ud55c \uac83\uc73c\ub85c \uc54c\ub824\uc84c\ub2e4. \uc2b9\ubb34\uc6d0\uc740 \uc67c\ucabd \uc5b4\uae68 \uace8\uc808\uc0c1\uc744 \uc785\uc5c8\uace0 \uba38\ub9ac\ub97c \ub2e4\ucce4\uc9c0\ub9cc \uc758\uc2dd\uc740 \ub69c\ub837\ud55c \uc0c1\ud669\uc774\ub77c\uace0"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: cb38c4bd-ce3f-49c4-9732-841dc5ff44e2
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 14438, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ub9e4\uace0 \uc788\uc5c8\uace0 \ube44\ud589\uae30\uac00 \ub2e4 \ucc29\ub959\ud55c \uac83 \uac19\uc558\ub294\ub370 \uadf8 \uc774\ud6c4\ub294 \uae30\uc5b5\uc774 \uc5c6\ub2e4\"\uace0\ub3c4 \ub9d0\ud55c \uac83\uc73c\ub85c \uc54c\ub824\uc84c\ub2e4. \uc2b9\ubb34\uc6d0\uc740 \uc67c\ucabd \uc5b4\uae68 \uace8\uc808\uc0c1\uc744 \uc785\uc5c8\uace0 \uba38\ub9ac\ub97c \ub2e4\ucce4\uc9c0\ub9cc \uc758\uc2dd\uc740 \ub69c\ub837\ud55c \uc0c1\ud669\uc774\ub77c\uace0 \ud55c\ub2e4. #\ud0d1\uc2b9\uac1d \uc911\uc5d0\ub294 KIA \ud0c0\uc774\uac70\uc988 \uad00\uacc4\uc790\ub4e4\ub3c4 \uc788\ub2e4\uace0 \ud558\uba70#, KIA \ud0c0\uc774\uac70\uc988 \uace0\uac15\uc778 \ud64d\ubcf4\ud300 \ucc45\uc784\ub9e4\ub2c8\uc800\uc640 \uac00\uc871\uc774 \ud0d1\uc2b9\ud55c \uac83\uc73c\ub85c \ud655\uc778\ub418\uc5c8\ub2e4. \uace0\uac15\uc778 \ub9e4\ub2c8\uc800\uc758 \uc790\ub140\uac00 \ucd5c\uc5f0\uc18c \ud76c\uc0dd\uc790\ub85c \ud655\uc778\ub410\uc73c\uba70, \uc81c\uc8fc\ud56d\uacf5\uc5d0 \uc794\uc5ec \uc88c\uc11d\uc774 \uc788\uae38\ub798 \uadc0\uad6d \uc77c\uc815\uc744 \ud558\ub8e8 \uc55e\ub2f9\uacbc\ub2e4\uac00 \ubcc0\uc744 \ub2f9\ud588\ub2e4\uace0 \ud55c\ub2e4. \ud568\uaed8 \uc5ec\ud589\uc744 \uac04 \ud615\uc740 \uc6d0\ub798 \uacc4\ud68d\ub300\ub85c \ud558\ub8e8 \ub354 \ud0dc\uad6d\uc5d0 \uba38\ubb3c\uae30\ub85c \uacb0\uc815\ud574 \uc0ac\uace0\ub97c \ud53c\ud588\ub2e4.6.1. \uc778\uba85 \ud53c\ud574 \uad00\ub828[\ud3b8\uc9d1]\uc5ed\ub300 \ub300\ud55c\ubbfc\uad6d\uc5d0\uc11c \ubc1c\uc0dd\ud55c \uc0ac\uace0 \uc911 \uc0ac\ub9dd\uc790\uc218 7\uc704[54]21\uc138\uae30 \ub300\ud55c\ubbfc\uad6d\uc5d0\uc11c \ubc1c\uc0dd\ud55c \uc0ac\uace0 \uc911 \uc0ac\ub9dd\uc790\uc218 3\uc704[55]2020\ub144\ub300 \ub300\ud55c\ubbfc\uad6d\uc5d0\uc11c \ubc1c\uc0dd\ud55c \uc0ac\uace0 \uc911 \uc0ac\ub9dd\uc790\uc218 1\uc704[56]2020\ub144\ub300 \uc77c\uc5b4\ub09c \uc804\uc138\uacc4 \ud56d\uacf5 \ucc38\uc0ac \uc911 \uc0ac\ub9dd\uc790\uc218 1\uc7042014\ub144 \uccad\ud574\uc9c4\ud574\uc6b4 \uc138\uc6d4\ud638 \uce68\ubab0 \uc0ac\uace0 \uc774\ud6c4 \ub300\ud55c\ubbfc\uad6d\uc5d0\uc11c \uac00\uc7a5 \ub9ce\uc740 \uc778\uba85\ud53c\ud574\uac00 \ubc1c\uc0dd\ud55c \uc0ac\uac74[57]\ub300\ud55c\ubbfc\uad6d \uc601\ud1a0 \ub0b4 \ud56d\uacf5 \uc0ac\uace0 \uc911 \uc0ac\ub9dd\uc790\uc218 1\uc704[58]\ub300\ud55c\ubbfc\uad6d \uad6d\uc801 \ud56d\uacf5\uc0ac\uac00 \uc5f0\ub8e8\ub41c \ucc38\uc0ac \uc911 \uc0ac\ub9dd\uc790\uc218 3\uc704[59]\uadf8 \uc678 \uc804\uc138\uacc4 \ud56d\uacf5 \ucc38\uc0ac \uc911\uc5d0\uc11c\ub3c4 41\uc704, \ud611\ub3d9\uccb4\uac00 \uc5f0\ub8e8\ub41c \ucc38\uc0ac \uc911\uc5d0\uc11c\ub294 13\uc704, 21\uc138\uae30 \ud56d\uacf5 \ucc38\uc0ac \uc911\uc5d0\uc11c\ub294 11\uc704, \ubcf4\uc789 737\uc774 \uc5f0\ub8e8\ub41c \ucc38\uc0ac \uc911\uc5d0\uc11c\ub294 2\uc704[60], \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 \uc911\uc5d0\uc11c\ub3c4 2\uc704[61]\ub97c \uae30\ub85d\ud55c \ub300\ud615 \uc0ac\uace0\uac00 \ub418\uc5c8\ub2e4.\ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c\ub85c \ucd94\uc815\ub418\uace0 \uc788\ub294 \uc0ac\uace0 \uc6d0\uc778\uc774 \uc0ac\uc2e4\ub85c \ud655\uc815\ub41c\ub2e4\uba74 \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c \uc0ac\uace0 \uc911\uc5d0\uc11c\ub294 1\uc704\uc5d0 \ub2ec\ud558\uac8c \ub420 \uac83\uc73c\ub85c \ubcf4\uc778\ub2e4.[62]7. \uad00\ub828 \ubcf4\ub3c4[\ud3b8\uc9d1]7.1. \ubc29\uc1a1[\ud3b8\uc9d1]SBS \uc18d\ubcf4\uc5f0\ud569\ub274\uc2a4 \uc18d\ubcf4\uc774 \uc0ac\uace0\ub85c \uc778\ud574 \uc9c0\uc0c1\ud30c 3\uc0ac[63]\uc758 TV \uc815\uaddc\ubc29\uc1a1\uc774 \ubaa8\ub450 \uc911\ub2e8\ub418\uace0 \ub274\uc2a4\ud2b9\ubcf4 \uccb4\uc81c\ub85c \uc804\ud658\ub418\uc5c8\ub2e4. SBS\uac00 \uc0ac\uace0\uac00 \uc77c\uc5b4\ub09c \uc9c0 \uc57d 27\ubd84 \ub9cc\uc778 \uc624\uc804 09\uc2dc 30\ubd84\uacbd\uc5d0 TV \ub3d9\ubb3c\ub18d\uc7a5 \ubc29\uc1a1 \uc804 \uad11\uace0 \uc1a1\ucd9c \ub3c4\uc911 \ub274\uc2a4 \uc18d\ubcf4 \uc790\ub9c9\uc744 \ud1b5\ud574 \uad00\ub828 \uc18c\uc2dd\uc744 \uc9c0\uc0c1\ud30c 3\uc0ac \ubc29\uc1a1\uad6d\uc911 \uac00\uc7a5 \ube60\ub974\uac8c \uc54c\ub838\ub2e4. \ub4a4\uc774\uc5b4 MBC\uac00 \uc624\uc804 09\uc2dc 39\ubd84\uacbd\uc5d0 \uce5c\uc808\ud55c \uc120\uc8fc\uc528 \uc7ac\ubc29\uc1a1 \ub3c4\uc911 \uc18d\ubcf4 \uc790\ub9c9\uc744"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: e5962541-127a-4c0c-a495-b12746c598b2
encoding: FLOAT32
metadata:
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created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 4c1e7183-4fdb-471f-b5ed-c4425ac51b5b
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{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 16240, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\ubc29\uc1a1\uc744 \uc870\uae30 \uc885\ub8cc\uc2dc\ud0a8 \uc774\uc720\ub294 \ubd88\uba85.kbc MyFM\uc740 \uc800\ub141 6\uc2dc\uc5d0 \ubc29\uc1a1\ud558\ub294 \ubba4\uc9c1\uc11c\ud551\uc758 \uc0ac\uc804 \ub179\uc74c\ubc29\uc1a1 \ud3b8\uc131\uc744 \ucde8\uc18c\ud558\uace0, \uc9c4\ud589\uc790 \uc5c6\uc774 \uc74c\uc545\ubc29\uc1a1\uc744 \ud558\uace0 \uc788\ub2e4. \ubba4\uc9c1\uc11c\ud551 \ubc29\uc1a1\uc2dc\uac01 \uc774\uc804\uc5d0 \uc0ac\uc804 \ub179\uc74c\ubc29\uc1a1\uc73c\ub85c \ubc29\uc1a1\ub418\ub294 \ubba4\uc9c1 \ud3ec\uc5d0\ubc84, \ucd94\uc5b5\ucc3e\uae30\ub294 \uc815\uc0c1\uc801\uc73c\ub85c \ubc29\uc1a1\ub410\ub2e4.MBC TV\ub294 2024 MBC \ubc29\uc1a1\uc5f0\uc608\ub300\uc0c1\uc758 \uc644\uc804 \ucde8\uc18c\uac00 \ud655\uc815\ub418\uc5c8\uace0[71] \uc815\uaddc \ud504\ub85c\uadf8\ub7a8\uc744 \uacb0\ubc29\ud55c \ub4a4 MBC \ub274\uc2a4\ud2b9\ubcf4\ub85c \ud3b8\uc131\uc744 \ubcc0\uacbd\ud588\ub2e4.KBS 1TV\ub294 TV\uc1fc \uc9c4\ud488\uba85\ud488, \uc804\uad6d\ub178\ub798\uc790\ub791 \ub4f1\uc744 \uacb0\ubc29\ud558\uace0 30\uc77c \uc0c8\ubcbd 3\uc2dc\uae4c\uc9c0 KBS \ub274\uc2a4\ud2b9\ubcf4\ub97c \uc5f0\uc18d\ud3b8\uc131\ud588\ub2e4.KBS 2TV\ub294 \uc0ac\uc7a5\ub2d8 \uadc0\ub294 \ub2f9\ub098\uadc0 \uadc0, 1\ubc15 2\uc77c, \ub354 \ub534\ub530\ub77c, \uac1c\uadf8\ucf58\uc11c\ud2b8 \ub4f1\uc744 \uacb0\ubc29\ud588\uace0 KBS 1TV \ud504\ub85c\uadf8\ub7a8\uc778 \uc0dd\ub85c\ubcd1\uc0ac\uc758 \ube44\ubc00, \uc0ac\ub791\uc758 \uac00\uc871 \ub4f1\uc73c\ub85c \ub300\uccb4\ud3b8\uc131\ud588\ub2e4.SBS TV\ub294 \ub7f0\ub2dd\ub9e8, \ubbf8\uc6b4 \uc6b0\ub9ac \uc0c8\ub07c \ub4f1\uc744 \uacb0\ubc29\ud558\uace0 SBS \ub274\uc2a4\ud2b9\ubcf4\ub85c \ud3b8\uc131\uc744 \ubcc0\uacbd\ud588\ub2e4.[72]JTBC\ub294 \ubb49\uccd0\uc57c \ucc2c\ub2e4 3, \ub0c9\uc7a5\uace0\ub97c \ubd80\ud0c1\ud574 since 2014\ub97c \uacb0\ubc29\ud588\uc73c\uba70 \uadf8 \uc2dc\uac04\ub300\uc5d0 \ub274\uc2a4\ud2b9\ubcf4\ub97c \ud3b8\uc131\ud588\ub2e4.\ub610\ud55c \uc0ac\uace0 \uc18c\uc2dd\uc744 \uc804\ud558\ub294 \ub274\uc2a4 \uc544\ub098\uc6b4\uc11c\ub4e4\uacfc \uac8c\uc2a4\ud2b8\ub85c \ucd08\ube59\ub41c \uc804\ubb38\uac00\ub4e4 \ub610\ud55c \uc774\ud0dc\uc6d0 \ucc38\uc0ac\ub54c\uc640 \ub9c8\ucc2c\uac00\uc9c0\ub85c \uac80\uc740\uc0c9 \uacc4\uc5f4\uc758 \uc637\uc744 \uc785\uace0 \ub274\uc2a4\ub97c \uc9c4\ud589\ud558\uba70 \uc560\ub3c4\uc758 \ub73b\uc744 \ud45c\ud558\uace0 \uc788\ub2e4.\uc0ac\uace0\ub85c \uc778\ud574 \uc5f0\ub9d0\uc5f0\uc2dc \ud589\uc0ac\ub294 \ucde8\uc18c\ub418\uac70\ub098 \ucd94\ubaa8 \ubd84\uc704\uae30 \uc18d\uc5d0\uc11c \uc9c4\ud589\ub420 \uac00\ub2a5\uc131\uc774 \uc788\ub2e4.[73]7.2. \uc678\uc2e0[\ud3b8\uc9d1]At least 28 dead after South Korean jet carrying 181 people crashes at airport, officials say( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 CNN)Plane burst into flames after skidding off runway at an airport in South Korea, killing at least 85( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 AP\ud1b5\uc2e0)At least 96 dead in fiery South Korea airliner crash( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 \ubbf8\uad6d\uc758 \uc18c\ub9ac)Jeju Air Accident in South Korea Kills at Least 47: Yonhap( \ubbf8\uad6d"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
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metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 17141, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "airliner crash( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 \ubbf8\uad6d\uc758 \uc18c\ub9ac)Jeju Air Accident in South Korea Kills at Least 47: Yonhap( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 Bloomberg)Plane veers off airport runway in South Korea and crashes, killing at least 120: reports( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 FOX NEWS)Plane crashes and bursts into flames at an airport in South Korea, killing at least 151( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 NPR)Live Updates: Jeju Air Flight Crashes in South Korea, Killing More Than 100( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 The New York Times)A plane veers off the runway and catches fire at a South Korean airport with reports of 28 dead( \ubbf8\uad6d \ud589\uc815\uad6c \uc18d\ub839 Chicago Tribune)At least 28 dead in South Korea plane crash - reports( \uc601\uad6d \ud589\uc815\uad6c \uc18d\ub839 BBC)Jeju Air crash: dozens killed after South Korean aircraft veers off runway and hits walls( \uc601\uad6d \ud589\uc815\uad6c \uc18d\ub839 The Guardian)South Korea plane crash live: Death toll rises to 124( \uc601\uad6d \ud589\uc815\uad6c \uc18d\ub839 The Daily Telegraph)South Korea plane crash: Jeju Air jet bursts into flames leaving at least 127 dead( \uc601\uad6d \ud589\uc815\uad6c \uc18d\ub839 The Times)At least 28 dead, two rescued in fiery aircraft crash in South Korea,"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 6e4df7ae-7eb4-4926-89b5-4a3433dfc16b
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 18037, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "127 dead( \uc601\uad6d \ud589\uc815\uad6c \uc18d\ub839 The Times)At least 28 dead, two rescued in fiery aircraft crash in South Korea, Yonhap reports( \uc601\uad6d \ud589\uc815\uad6c \uc18d\ub839 Reuters)Dozens killed in South Korea after plane veers off runway, bursts into flames( \uce90\ub098\ub2e4 \ud589\uc815\uad6c \uc18d\ub839 CBC)\u97d3\u56fd\u306e\u52d9\u5b89\uff08\u30e0\u30a2\u30f3\uff09\u56fd\u969b\u7a7a\u6e2f\u3067\u65c5\u5ba2\u6a5f\u304c\u6ed1\u8d70\u8def\u5916\u308c\u885d\u7a81\u4e8b\u6545\u6b7b\u800528\u4eba\u3092\u78ba\u8a8d( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 ANN)\u3010\u901f\u5831\u4e2d\u3011\u97d3\u56fd\u5357\u897f\u90e8\u30e0\u30a2\u30f3\u3067\u65c5\u5ba2\u6a5f\u304c\u708e\u4e0a 47\u4eba\u6b7b\u4ea1( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 NHK)127\u4eba\u6b7b\u4ea1\u30012\u4eba\u6551\u52a9\u3000\u97d3\u56fd\u3067\u65c5\u5ba2\u6a5f\u304c\u80f4\u4f53\u7740\u9678\u5f8c\u306b\u708e\u4e0a( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 \uc544\uc0ac\ud788 \uc2e0\ubb38)\u97d3\u56fd\u30fb\u52d9\u5b89\u7a7a\u6e2f\u3067\u65c5\u5ba2\u6a5f\u304c\u7740\u9678\u5931\u6557\u3001\uff12\uff18\u4eba\u6b7b\u4ea1\u2026\u731b\u30b9\u30d4\u30fc\u30c9\u306e\u307e\u307e\u6ed1\u8d70\u8def\u306e\u58c1\u306b\u6fc0\u7a81\u3057\u708e\u4e0a( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 \uc694\ubbf8\uc6b0\ub9ac \uc2e0\ubb38)\u97d3\u56fd\u306e\u65c5\u5ba2\u6a5f\u4e8b\u6545\u3000\u80f4\u4f53\u7740\u9678\u306b\u5931\u6557\u304b\u3000\u6b7b\u8005\u306f47\u4eba\u306b( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 \ub9c8\uc774\ub2c8\uce58 \uc2e0\ubb38)\u97d3\u56fd\u5357\u897f\u90e8\u306e\u7a7a\u6e2f\u3067\u65c5\u5ba2\u6a5f\u4e8b\u6545\uff64120\u4eba\u6b7b\u4ea1\u3000181\u4eba\u304c\u642d\u4e57( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 \ub2c8\ud63c\uac8c\uc774\uc790\uc774 \uc2e0\ubb38)\u30d0\u30fc\u30c9\u30b9\u30c8\u30e9\u30a4\u30af\u304c\u539f\u56e0\u304b\u3000181\u4eba\u642d\u4e57\u306e\u97d3\u56fd\u65c5\u5ba2\u6a5f\u30002\u4eba\u6551\u52a9\u3001\u6b8b\u308b\u5927\u534a\u306e\u751f\u5b58\u7d76\u671b\u7684( \uc77c\ubcf8 \ud589\uc815\uad6c \uc18d\ub839 \uc0b0\ucf00\uc774 \uc2e0\ubb38)\u97e9\u56fd\u4e00\u8f7d\u6709175\u540d\u4e58\u5ba2\u7684\u5ba2\u673a\u7740\u9646\u65f6\u649e\u5899\u8d77\u706b( \uc911\uad6d \ud589\uc815\uad6c \uc18d\ub839 \ud658\uad6c\uc2dc\ubcf4)Plane erupts into fireball shortly after landing in South Korea( \uc911\uad6d \ud589\uc815\uad6c \uc18d\ub839 CGTN)[\u4e2d\u56fd\u65b0\u95fb]\u97e9\u56fd\u4e00\u67b6\u5ba2\u673a\u7740\u9646\u65f6\u649e\u5899\u7206\u70b8\u8d77\u706b( \uc911\uad6d \ud589\uc815\uad6c \uc18d\ub839 CCTV)\u97d3\u570b\u7a7a\u96e3\uff5c\u6fdf\u5dde\u822a\u7a7a\u5ba2\u6a5f\u964d\u843d\u5931\u4e8b\u300085\u4eba\u55aa\u751f\u5169\u4eba\u7372\u6551\u3000\u6a5f\u9ad4\u65b7\u5169\u622a\u8d77\u706b\u3000\u8d77\u56e0\u7591\u9ce5\u64ca( \ud64d\ucf69 \ud589\uc815\uad6c \uc18d\ub839 \uba85\ubcf4)85 dead in South Korea Jeju Air plane crash( \ud64d\ucf69 \ud589\uc815\uad6c \uc18d\ub839 SCMP)\u97d3\u7a7a\u96e3\u589e\u81f3125\u6b7b \u6a5f\u5834\u66fe\u5411\u5ba2\u6a5f\u767c\u9ce5\u7fa4\u8b66\u5831( \ub9c8\uce74\uc624 \ud589\uc815\uad6c \uc18d\ub839 TDM)\u6fdf\u5dde\u822a\u7a7a\u56de\u61c9\u4e86\uff01\u5931\u4e8b\u5ba2\u6a5f\u88ab\u8d77\u5e95\u6a5f\u9f6115\u5e74 40\u67b6\u6a5f\u968a\u300c\u670938\u67b6\u662f\u540c\u6b3e\u300d( \ub300\ub9cc \ud589\uc815\uad6c \uc18d\ub839 \uc5f0\ud569\ubcf4)\u5357\u97d3\u7a7a\u96e3\uff01 \u6d88\u9632\u5ef3\uff1a2\u4eba\u7372\u6551 \u5176\u9918179\u4eba\u6050\u5168\u6578\u7f79\u96e3( \ub300\ub9cc \ud589\uc815\uad6c \uc18d\ub839 \uc790\uc720\uc2dc\ubcf4)Tai n\u1ea1n m\u00e1y"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 707f76f3-b02c-4f13-8f35-412f70c786a9
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 18952, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "40\u67b6\u6a5f\u968a\u300c\u670938\u67b6\u662f\u540c\u6b3e\u300d( \ub300\ub9cc \ud589\uc815\uad6c \uc18d\ub839 \uc5f0\ud569\ubcf4)\u5357\u97d3\u7a7a\u96e3\uff01 \u6d88\u9632\u5ef3\uff1a2\u4eba\u7372\u6551 \u5176\u9918179\u4eba\u6050\u5168\u6578\u7f79\u96e3( \ub300\ub9cc \ud589\uc815\uad6c \uc18d\ub839 \uc790\uc720\uc2dc\ubcf4)Tai n\u1ea1n m\u00e1y bay \u1edf H\u00e0n Qu\u1ed1c: X\u00e1c \u0111\u1ecbnh nguy\u00ean nh\u00e2n ban \u0111\u1ea7u, s\u1ed1 ng\u01b0\u1eddi thi\u1ec7t m\u1ea1ng c\u00f3 th\u1ec3 t\u0103ng m\u1ea1nh( \ubca0\ud2b8\ub0a8 \ud589\uc815\uad6c \uc18d\ub839 VTV)\u0e14\u0e48\u0e27\u0e19! \u0e40\u0e04\u0e23\u0e37\u0e48\u0e2d\u0e07\u0e1a\u0e34\u0e19 Jeju Air \u0e44\u0e16\u0e25\u0e23\u0e31\u0e19\u0e40\u0e27\u0e22\u0e4c\u0e15\u0e32\u0e22\u0e40\u0e1a\u0e37\u0e49\u0e2d\u0e07\u0e15\u0e49\u0e19 28 \u0e04\u0e19( \ud0dc\uad6d \ud589\uc815\uad6c \uc18d\ub839 Thai PBS)Fatalities from the Thailand-bound plane crash in South Korea rise to 62( \ud0dc\uad6d \ud589\uc815\uad6c \uc18d\ub839 The Nation Thailand)At least 151 dead in South Korea plane crash( \ud0dc\uad6d \ud589\uc815\uad6c \uc18d\ub839 Bangkok Post)Death toll from Jeju Air crash in South Korea crosses 150( \uc2f1\uac00\ud3ec\ub974 \ud589\uc815\uad6c \uc18d\ub839 CNA)South Korean plane with 181 passengers catches fire at Muan airport; media report 120 people dead( \uc778\ub3c4 \ud589\uc815\uad6c \uc18d\ub839 The Hindu)At least 85 killed in S Korea as plane crashes on landing at Muan airport( \uce74\ud0c0\ub974 \ud589\uc815\uad6c \uc18d\ub839 Al Jazeera)G\u00fcney Kore\u2019de yolcu u\u00e7a\u011f\u0131 kaza yapt\u0131: En az 122 \u00f6l\u00fc( \ud280\ub974\ud0a4\uc608 \ud589\uc815\uad6c \uc18d\ub839 TRT)\u062a\u062d\u0637\u0645 \u0637\u0627\u0626\u0631\u0629 \u062a\u0642\u0644 181 \u0631\u0627\u0643\u0628\u0627 \u0641\u064a \u0645\u0637\u0627\u0631 \u0645\u0648\u0627\u0646 \u0627\u0644\u062f\u0648\u0644\u064a \u0628\u0643\u0648\u0631\u064a\u0627 \u0627\u0644\u062c\u0646\u0648\u0628\u064a\u0629( \uc774\uc9d1\ud2b8 \ud589\uc815\uad6c \uc18d\ub839 Al Qahera Al Youm)\u0645\u0642\u062a\u0644 151 \u0634\u062e\u0635\u0627 \u0639\u0644\u0649 \u0627\u0644\u0623\u0642\u0644 \u0641\u064a \u062a\u062d\u0637\u0645 \u0637\u0627\u0626\u0631\u0629 \u0628\u0643\u0648\u0631\u064a\u0627 \u0627\u0644\u062c\u0646\u0648\u0628\u064a\u0629( \ubaa8\ub85c\ucf54 \ud589\uc815\uad6c \uc18d\ub839 SNRT)At least 127 dead after South Korean airliner"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 08040b69-1d8a-4694-a336-1916b54b3ee3
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 19853, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "\u0627\u0644\u0623\u0642\u0644 \u0641\u064a \u062a\u062d\u0637\u0645 \u0637\u0627\u0626\u0631\u0629 \u0628\u0643\u0648\u0631\u064a\u0627 \u0627\u0644\u062c\u0646\u0648\u0628\u064a\u0629( \ubaa8\ub85c\ucf54 \ud589\uc815\uad6c \uc18d\ub839 SNRT)At least 127 dead after South Korean airliner belly-landed at airport.( \ub0a8\uc544\ud504\ub9ac\uce74 \uacf5\ud654\uad6d \ud589\uc815\uad6c \uc18d\ub839 SABC)179 people on board plane that skidded off runway in South Korea now presumed dead( \uc720\ub7fd\uc5f0\ud569 \ud589\uc815\uad6c \uc18d\ub839 Euronews)Viele Tote bei Flugzeugungl\u00fcck in S\u00fcdkorea( \ub3c5\uc77c \ud589\uc815\uad6c \uc18d\ub839 ARD)Flugzeug-Ungl\u00fcck in S\u00fcdkorea: Viele Tote( \ub3c5\uc77c \ud589\uc815\uad6c \uc18d\ub839 ZDF)South Korea: Jeju Air plane crash death toll rises further( \ub3c5\uc77c \ud589\uc815\uad6c \uc18d\ub839 Deutsche Welle)Passagierflugzeug bei Landung in S\u00fcdkorea verungl\u00fcckt( \uc2a4\uc704\uc2a4 \ud589\uc815\uad6c \uc18d\ub839 SRG SSR)Vliegtuig crasht bij landing in Zuid-Korea, zeker 151 doden( \ub124\ub35c\ub780\ub4dc \ud589\uc815\uad6c \uc18d\ub839 NOS)En Cor\u00e9e du Sud, un avion transportant 181 personnes s\u2019embrase \u00e0 l\u2019atterrissage, faisant au moins soixante-deux morts( \ud504\ub791\uc2a4 \ud589\uc815\uad6c \uc18d\ub839 Le Monde)Un accident d'avion fait au moins 120 morts en Cor\u00e9e du Sud, seuls deux rescap\u00e9s secourus( \ud504\ub791\uc2a4 \ud589\uc815\uad6c \uc18d\ub839 Franceinfo:)More than 150 people killed as plane crash-lands in South Korean airport( \ud504\ub791\uc2a4 \ud589\uc815\uad6c \uc18d\ub839 France 24)Corea del Sud, un aereo si schianta in"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: b4376d5e-16f8-4483-b44d-56fb355be39c
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 20745, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "crash-lands in South Korean airport( \ud504\ub791\uc2a4 \ud589\uc815\uad6c \uc18d\ub839 France 24)Corea del Sud, un aereo si schianta in atterraggio. Almeno 96 morti( \uc774\ud0c8\ub9ac\uc544 \ud589\uc815\uad6c \uc18d\ub839 RAI)Al menos 120 muertos en Corea del Sur tras estrellarse un avi\u00f3n en el aeropuerto de Muan( \uc2a4\ud398\uc778 \ud589\uc815\uad6c \uc18d\ub839 RTVE)7h Despiste de avi\u00e3o na Coreia do Sul matou maior parte dos passageiros( \ud3ec\ub974\ud22c\uac08 \ud589\uc815\uad6c \uc18d\ub839 RTP)South Korea Jeju Air plane crash death toll rises to 151( \uc544\uc77c\ub79c\ub4dc \ud589\uc815\uad6c \uc18d\ub839 RT\u00c9)\u0391\u03b5\u03c1\u03bf\u03c0\u03bf\u03c1\u03b9\u03ba\u03ae \u03c4\u03c1\u03b1\u03b3\u03c9\u03b4\u03af\u03b1 \u03c3\u03c4\u03b7 \u039d\u03cc\u03c4\u03b9\u03b1 \u039a\u03bf\u03c1\u03ad\u03b1: \u03a6\u03cc\u03b2\u03bf\u03b9 \u03cc\u03c4\u03b9 \u03bc\u03cc\u03bb\u03b9\u03c2 2 \u03b1\u03c0\u03cc \u03c4\u03bf\u03c5\u03c2 181 \u03b5\u03c0\u03b9\u03b2\u03b1\u03af\u03bd\u03bf\u03bd\u03c4\u03b5\u03c2 \u03b5\u03c0\u03ad\u03b6\u03b7\u03c3\u03b1\u03bd( \uadf8\ub9ac\uc2a4 \ud589\uc815\uad6c \uc18d\ub839 ERT)P\u0159i hav\u00e1rii letadla v Ji\u017en\u00ed Koreji zem\u0159elo nejm\u00e9n\u011b 151 lid\u00ed( \uccb4\ucf54 \ud589\uc815\uad6c \uc18d\ub839 \u010cesk\u00e1 televize)Csak ketten \u00e9lt\u00e9k t\u00fal a d\u00e9l-koreai rep\u00fcl\u0151g\u00e9p-katasztr\u00f3f\u00e1t( \ud5dd\uac00\ub9ac \ud589\uc815\uad6c \uc18d\ub839 H\u00edr TV)Cel pu\u0163in 151 de mor\u0163i \u00een urma pr\u0103bu\u015firii unui avion \u00een Coreea de Sud( \ub8e8\ub9c8\ub2c8\uc544 \ud589\uc815\uad6c \uc18d\ub839 TVR)N\u00e4rmare 180 befaras d\u00f6da i flygkrasch i Sydkorea( \uc2a4\uc6e8\ub374 \ud589\uc815\uad6c \uc18d\ub839 SVT)Minst 96 d\u00f8de da fly krasjet i S\u00f8r-Korea( \ub178\ub974\uc6e8\uc774 \ud589\uc815\uad6c \uc18d\ub839 NRK)Fly med 181 ombord forulykker under landing i Sydkorea - flere meldes dr\u00e6bt( \ub374\ub9c8\ud06c \ud589\uc815\uad6c \uc18d\ub839 DR)\u041f\u043e\u0447\u0442\u0438 \u0432\u0441\u0435"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 9aa22ee7-0c0a-4c87-b27f-b60d8d228ce1
encoding: FLOAT32
metadata:
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created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 503a56d5-4dc6-4db1-8879-756179bce85e
encoding: FLOAT32
metadata:
{"source": "https://namu.wiki/w/%EC%A0%9C%EC%A3%BC%ED%95%AD%EA%B3%B5%202216%ED%8E%B8%20%ED%99%9C%EC%A3%BC%EB%A1%9C%20%EC%9D%B4%ED%83%88%20%EC%82%AC%EA%B3%A0", "title": "\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0 - \ub098\ubb34\uc704\ud0a4", "language": "ko", "start_index": 22535, "embedding_config": "{\"engine\": \"\", \"model\": \"jhgan/ko-sroberta-multitask\"}", "chroma:document": "del Sur( \ud398\ub8e8 \ud589\uc815\uad6c \uc18d\ub839 TV Per\u00fa)\u0628\u0627\u0644\u0641\u064a\u062f\u064a\u0648.. \u062a\u062d\u0637\u0645 \u0637\u0627\u0626\u0631\u0629 \u0628\u0643\u0648\u0631\u064a\u0627 \u0627\u0644\u062c\u0646\u0648\u0628\u064a\u0629 \u0648\u0645\u0635\u0631\u0639 120 \u0645\u0646 \u0631\u0643\u0627\u0628\u0647\u0627 ( \ud314\ub808\uc2a4\ud0c0\uc778 \ud589\uc815\uad6c \uc18d\ub839 Al-quds)8. \uad00\ub828 \ubb38\uc11c[\ud3b8\uc9d1]\ucc29\ub959\ubcf5\ud589\ub3d9\uccb4 \ucc29\ub959\uc624\ubc84\ub7f08.1. \uc720\uc0ac \uc0ac\uace0[\ud3b8\uc9d1]\uc720\ub098\uc774\ud2f0\ub4dc \ud56d\uacf5 232\ud3b8 \ucc29\ub959 \uc0ac\uace0 - \uc0ac\uace0 \uc6d0\uc778\uc73c\ub85c \ucd94\uc815\ub418\ub294 \uc720\uc555\uacc4\ud1b5 \uc190\uc2e4 \uc0c1\ud0dc\ub85c \ucc29\ub959\ud558\ub2e4 \uae30\uccb4\uac00 \ud30c\uad34\ub41c \uc0ac\uace0\ub2e4.\uc77c\ubcf8\ud56d\uacf5 123\ud3b8 \ucd94\ub77d \uc0ac\uace0 - \ub9c8\ucc2c\uac00\uc9c0\ub85c \uc720\uc555\uacc4\ud1b5 \uc190\uc2e4 \uc0c1\ud0dc\ub85c \uc5d4\uc9c4\uc73c\ub85c \uc870\uc885\ud558\uc5ec \ucc29\ub959\uc2dc\ub3c4\ud558\uc600\uc73c\ub098 \uc0b0\uc5d0 \ucda9\ub3cc\ud558\uba74\uc11c \uae30\uccb4\uac00 \ud30c\uad34\ub418\uace0 \ub2e4\uc218\uc758 \uc0ac\uc0c1\uc790\ub97c \ub0b8 \uc0ac\uace0\ub2e4.[74]\uc5d0\uc5b4 \ud504\ub791\uc2a4 007\ud3b8 \ucd94\ub77d\uc0ac\uace0 - \ud65c\uc8fc\ub85c \uc774\ud0c8, \uc2b9\ubb34\uc6d0 2\uba85\ub9cc \uc0dd\uc874\uc774 \uc720\uc0ac\ud55c \ud56d\uacf5\uc0ac\uace0\ub2e4.TAP \ud3ec\ub974\ud22c\uac08 \ud56d\uacf5 425\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace0\uacf5\uad70 \uc11c\uc0b0\uae30\uc9c0 F-35A \ub3d9\uccb4\ucc29\ub959 \uc0ac\uace0 - \uc774 \uc0ac\uace0\ub294 \ubbfc\ud56d\uae30\uc778 \ubcf8 \uc0ac\uace0\uc640\ub294 \ub2e4\ub974\uac8c \uad70\uc6a9 \uc804\ud22c\uae30\uc778 F-35\uc758 \uc0ac\uace0\ub77c\ub294 \ucc28\uc774\uc810\uc774 \uc788\uc9c0\ub9cc \ubc84\ub4dc \uc2a4\ud2b8\ub77c\uc774\ud06c\ub85c \uc778\ud574 \uc720\uc555\uacc4\ud1b5\uacfc \uc804\uc790\uc7a5\ube44\uac00 \ubaa8\ub450 \ud30c\uc190\ub418\uc5b4 \ub79c\ub529 \uae30\uc5b4\uac00 \ub0b4\ub824\uc624\uc9c0 \uc54a\uc544 \ub3d9\uccb4\ucc29\ub959\uc744 \ud588\ub2e4\ub294 \uacf5\ud1b5\uc810\uc774 \uc788\ub2e4.9. \ub458\ub7ec\ubcf4\uae30[\ud3b8\uc9d1] \uc5ed\ub300 \ub300\ud55c\ubbfc\uad6d\uc758 \ub300\ud615 \uc0ac\uace0 \ubc0f \ucc38\uc0ac [ 2020\ub144\ub300 ]\ubc1c\uc0dd\uc77c\uc0ac\ub9dd\uc0ac\uac74\uba852024\ub144 12\uc6d4 29\uc77c179\uc81c\uc8fc\ud56d\uacf5 2216\ud3b8 \ud65c\uc8fc\ub85c \uc774\ud0c8 \uc0ac\uace02024\ub144 6\uc6d4 24\uc77c23\ud654\uc131 \uc77c\ucc28\uc804\uc9c0 \uc81c\uc870\uacf5\uc7a5 \ud654\uc7ac \uc0ac\uace02022\ub144 10\uc6d4 29\uc77c159\uc774\ud0dc\uc6d0 \uc555\uc0ac \uc0ac\uace02020\ub144 4\uc6d4 29\uc77c38\ud55c\uc775\uc2a4\ud504\ub808\uc2a4 \uc774\ucc9c \ubb3c\ub958\uc13c\ud130 \ud654\uc7ac \uc0ac\uace0 [ 2010\ub144\ub300 ]\ubc1c\uc0dd\uc77c\uc0ac\ub9dd\uc0ac\uac74\uba852019\ub144 5\uc6d4 29\uc77c27 25\ud5dd\uac00\ub9ac \uc720\ub78c\uc120 \uce68\ubab0 \uc0ac\uace02018\ub144 1\uc6d4 26\uc77c51\ubc00\uc591 \uc138\uc885\ubcd1\uc6d0 \ud654\uc7ac \uc0ac\uace02017\ub144 12\uc6d4 21\uc77c29\uc81c\ucc9c \uc2a4\ud3ec\uce20\uc13c\ud130 \ud654\uc7ac \uc0ac\uace02017\ub144 3\uc6d4 31\uc77c 22\uc2a4\ud154\ub77c \ub370\uc774\uc9c0\ud638 \uce68\ubab0 \uc0ac\uace02014\ub144 12\uc6d4 1\uc77c27501\uc624\ub8e1\ud638 \uce68\ubab0\uc0ac\uace02014\ub144 5\uc6d4 28\uc77c21\uc7a5\uc131 \ud6a8\uc0ac\ub791\uc694\uc591\ubcd1\uc6d0 \ud654\uc7ac \uc0ac\uace02014\ub144 4\uc6d4 16\uc77c299\uccad\ud574\uc9c4\ud574\uc6b4 \uc138\uc6d4\ud638 \uce68\ubab0 \uc0ac\uace02010\ub144 12\uc6d4 13\uc77c22\uc81c1\uc778\uc131\ud638 \uce68\ubab0 \uc0ac\uace02010\ub144 3\uc6d4 26\uc77c46\ucc9c\uc548\ud568 \ud53c\uaca9 \uc0ac\uac74 [ 2000\ub144\ub300 ]\ubc1c\uc0dd\uc77c\uc0ac\ub9dd\uc0ac\uac74\uba852008\ub144 1\uc6d4 7\uc77c40\uc774\ucc9c"}


created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
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created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: c9bda3ed-10b3-4e58-a108-12c77aa823a4
encoding: FLOAT32
metadata:
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created_at: 2024-12-29 13:56:07
topic: persistent://default/default/a4e4adcc-d9b7-45b7-8ba1-4af4b8c6d9f2
id: 5e47d65e-dd35-49d1-ab00-64282cc17f5c
encoding: FLOAT32
metadata:
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