25 research outputs found

    Korean Dependency Parsing Reflected Chunking

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    ์ž์—ฐ์–ธ์–ด์ฒ˜๋ฆฌ์—์„œ ๊ตฌ๋ฌธ๋ถ„์„์€ ๋ฌธ์žฅ ๊ตฌ์„ฑ ์„ฑ๋ถ„๋“ค์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ณผ์ •์„ ๋งํ•œ๋‹ค. ๊ตฌ๋ฌธ๋ถ„์„์„ ํ†ตํ•ด ๋ฌธ์žฅ์˜ ๊ตฌ์กฐ๋ฅผ ๊ฒฐ์ •ํ•จ์œผ๋กœ์จ ์˜๋ฏธ์  ์ค‘์˜์„ฑ์„ ํ•ด์†Œํ•  ์ˆ˜ ์žˆ๋‹ค. ํ•œ๊ตญ์–ด ๊ตฌ๋ฌธ๋ถ„์„์€ ๊ตฌ๋ฌธ๋ถ„์„๊ธฐ์˜ ์ž…๋ ฅ์ด ๋˜๋Š” ๋ฌธ์žฅ์˜ ์„ฑ๋ถ„ ์ˆ˜๊ฐ€ ๋งŽ์•„ ์ด๋กœ ์ธํ•ด ๋ถ„์„์˜ ๋ณต์žก๋„๊ฐ€ ๋†’๊ณ  ์ •ํ™•๋„๊ฐ€ ๋‚ฎ์€ ํ˜„์ƒ์„ ๋ณด์ธ๋‹ค. ์ด์— ๋Œ€ํ•œ ํ•ด๊ฒฐ๋ฐฉ์•ˆ์œผ๋กœ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ํ•œ๊ตญ์–ด ๊ตฌ๋ฌธ๋ถ„์„์„ ์ œ์•ˆํ•œ๋‹ค. ๊ตฌ๋ฌถ์Œ์€ ํ˜•ํƒœ์†Œ๋ถ„์„๋œ ๋ฌธ์žฅ์— ๋Œ€ํ•ด ๋ฌธ๋ฒ•์ , ์˜๋ฏธ์ ์œผ๋กœ ํ•˜๋‚˜์˜ ์—ญํ• ์„ ํ•˜๋Š” ์—ฐ์†๋œ ํ˜•ํƒœ์†Œ๋“ค์„ ํ•˜๋‚˜์˜ ๋ง๋ฉ์ด๋กœ ๋ฌถ๋Š” ์ž‘์—…์„ ๋งํ•œ๋‹ค. ๊ตฌ๋ฌถ์Œ์„ ์ˆ˜ํ–‰ํ•˜๋ฉด ๊ตฌ๋ฌธ๋ถ„์„์˜ ์ž…๋ ฅ์ด ๋˜๋Š” ๋ฌธ์žฅ ์„ฑ๋ถ„์˜ ์ˆ˜๊ฐ€ ์ค„์–ด๋“ค๋ฉฐ, ๋ฌธ์žฅ ๋‚ด์—์„œ ๋ณด์กฐ์ ์ธ ์—ญํ• ์„ ํ•˜๋Š” ์š”์†Œ๋“ค์ด ํ•˜๋‚˜์˜ ๋ง๋ฉ์ด๋กœ ๋ฌถ์ด๋ฏ€๋กœ ๋ง๋ฉ์ด ๋‚ด์˜ ์ค‘์‹ฌ์–ด์— ๋Œ€ํ•ด์„œ๋งŒ ์˜์กด ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์–ด ๊ตฌ๋ฌธ๋ถ„์„์˜ ํšจ์œจ์„ฑ์ด ์ฆ์ง„๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๊ตฌ๋ฌธ๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ํ•œ๊ตญ์–ด์— ๋Œ€ํ•ด ๊ตฌ๋ฌถ์Œ๊ณผ ๋ง๋ฉ์ด๋ฅผ ์ •์˜ํ•˜๊ณ  ์ด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ๊ตฌ๋ฌถ์Œ์„ ์ˆ˜ํ–‰ํ•œ๋‹ค. ๋˜ํ•œ, ๊ตฌ๋ฌถ์Œ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ธฐ์กด์˜ ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ง๋ญ‰์น˜๋กœ๋ถ€ํ„ฐ ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ์˜์กด๊ตฌ์กฐ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ์ž‘์—…์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ ๊ถ๊ทน์ ์œผ๋กœ ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๊ตฌ๋ฌธ๋ถ„์„๊ณผ ๊ธฐ์กด์˜ ๊ตฌ๋ฌธ๋ถ„์„์„ ๋น„๊ตํ•˜๊ณ  ๋ถ„์„ํ•จ์œผ๋กœ์จ ํ•œ๊ตญ์–ด์ฒ˜๋ฆฌ์— ์žˆ์–ด ๊ตฌ๋ฌถ์Œ์˜ ์œ ํšจ์„ฑ๊ณผ ํ•„์š”์„ฑ์„ ๋ณด์ด๋Š” ๋ฐ ๊ทธ ์˜์˜๊ฐ€ ์žˆ๋‹ค. ์‹คํ—˜ ๊ฒฐ๊ณผ ์–ด์ ˆ ๋‹จ์œ„๋กœ ์ •ํ™•๋„๋ฅผ ์ธก์ •ํ–ˆ์„ ๋•Œ, ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๊ฒฝ์šฐ๋Š” UAS ๊ธฐ์ค€ 86.48%, LAS ๊ธฐ์ค€ 84.56% ์˜€์œผ๋ฉฐ, ๊ธฐ์กด ๋ฐฉ์‹์˜ ๊ฒฝ์šฐ UAS ๊ธฐ์ค€ 82.98%, LAS ๊ธฐ์ค€ 80.45%๋กœ, ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๊ฒฝ์šฐ๊ฐ€ ๊ฐ๊ฐ 3.5%p, 4.11%p ์ƒ์Šนํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€๋‹ค. ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๊ตฌ๋ฌธ๋ถ„์„์€ ์ •ํ™•๋„๋‚˜ ํšจ์œจ์„ฑ ๋ฉด์—์„œ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๋ณด๋‹ค ๋‚˜์€ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์˜€์œผ๋ฉฐ, ๊ตฌ๋ฌธ์ ์ธ ๊ด€์ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์˜๋ฏธ์ ์ธ ์š”์†Œ๋„ ํ•จ๊ป˜ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ๋ฒ•์ด๋‹ค. ๋”ฐ๋ผ์„œ ํ•œ๊ตญ์–ด์ฒ˜๋ฆฌ์—์„œ๋„ ์ง€์†์ ์œผ๋กœ ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๊ตฌ๋ฌธ๋ถ„์„์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ตฌ๋ฌถ์Œ ์ž์ฒด์— ๋Œ€ํ•œ ์˜ค๋ฅ˜ ๋ถ„์„๊ณผ ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ๋ง๋ญ‰์น˜์˜ ํšจ์šฉ์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋„ ๋‹ค๊ฐ๋„์—์„œ ๊ฒ€์ฆ๋˜์–ด์•ผ ํ•  ๊ฒƒ์ด๋‹ค. ๋˜ํ•œ, ๋‚ด์šฉ์–ด์™€ ๊ธฐ๋Šฅ์–ด์˜ ๋น„์ค‘์ด ๊ตฌ๋ฌธ๋ถ„์„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ๋„ ํฅ๋ฏธ ์žˆ๋Š” ์ฃผ์ œ๋กœ ๋‚จ์•„์žˆ๋‹ค.|In natural language processing, syntactic parsing is to analyze relationship between sentence components. The parsing can resolve semantic as well as syntactic ambiguity by determining the relationship. On the other hand, in Korean parsing, usually there are a lot of components (or morphemes) in an input sentence, and these can cause high complexity and low accuracy in parsing. To alleviate this problem, we propose Korean parsing reflected chunking. Chunking is to identify constituents called chunks which are a sequence of words (or morphemes) playing a syntactic and semantic role in a given sentence. We can decrease the number of the input components of the parser by chunking. Moreover, chunking groups morphemes with auxiliary meaning like functional or grammatical meaning, so we can just focus on the head word in chunks. The purpose of this paper is therefore threefold. The first is to define Korean chunks. The second is to build Korean dependency corpus reflected chunking, which is for experiments, according to the chunk definition. The corpus can be automatically converted from the existing Korean dependency corpus. The third is to develop a Korean dependency parser reflected chunking. The parser has been experimentally evaluated in parsing Korean text, achieving UAS and LAS of 86.48% and 84.56% respectively. The parser outperforms the Korean parser which is not reflected chunking by 3.5%p and 4.11%p, and has been shown to be better than the existing one in performance. The parser can also analyze semantic as well as syntactic structure. In the future, the study on chunking in Korean should be conducted consistently for establishing linguistic concepts. An error analysis on the chunking and parsing is required for performance improvement. Furthermore, the difference in vector representation according to the ratio between content chunks and function chunks in a sentence still remains as an interesting subject.๋ชฉ ์ฐจ List of Tables iv List of Figures v Abstract vi ์ดˆ๋ก viii ์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 2 ์žฅ ๊ด€๋ จ ์—ฐ๊ตฌ 4 2.1 ์˜์กด๊ตฌ์กฐ ๋ถ„์„ 4 2.1.1 ์˜์กด๊ตฌ์กฐ ๋ถ„์„ ๋ฐฉ๋ฒ•๋ก  5 2.1.2 ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ถ„์„ 8 2.2 ๊ตฌ๋ฌถ์Œ 10 2.2.1 ๊ตฌ๋ฌถ์Œ๊ณผ ๋ง๋ฉ์ด 10 2.2.2 ํ•œ๊ตญ์–ด ๊ตฌ๋ฌถ์Œ 11 2.3 ์˜์กด๊ตฌ์กฐ ๋ถ„์„ ๋ง๋ญ‰์น˜ 12 2.3.1 UD ๋ง๋ญ‰์น˜ 13 2.3.2 ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ง๋ญ‰์น˜ 13 ์ œ 3 ์žฅ ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ถ„์„ 15 3.1 ํ•œ๊ตญ์–ด ๋ง๋ฉ์ด 16 3.1.1 ํ•œ๊ตญ์–ด ๋ง๋ฉ์ด์˜ ์ •์˜ 16 3.1.2 ๋ง๋ฉ์ด ์ข…๋ฅ˜ ๋ฐ ํ‘œ์ง€ 18 3.2 ํ•œ๊ตญ์–ด ๊ตฌ๋ฌถ์Œ 19 3.2.1 ๊ตฌ๋ฌถ์Œ ๋ง๋ญ‰์น˜ ๊ตฌ์ถ• 19 3.2.2 ์‹ฌ์ธตํ•™์Šต์„ ์ด์šฉํ•œ ํ•œ๊ตญ์–ด ๊ตฌ๋ฌถ์Œ 20 3.3 ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ง๋ญ‰์น˜ ์ƒ์„ฑ 21 3.3.1 ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ์˜์กด๊ตฌ์กฐ ๋ง๋ญ‰์น˜ 21 3.3.2 ๋ง๋ญ‰์น˜ ๋ณ€ํ™˜ ๊ณผ์ • ๋ฐ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 23 3.3.3 ๋ง๋ญ‰์น˜ ๋ณ€ํ™˜ ๊ฒฐ๊ณผ ๋ถ„์„ 29 3.4 ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ถ„์„ 31 3.4.1 ์˜์กด๊ตฌ์กฐ ๋ถ„์„ ๋ชจ๋ธ 31 3.4.2 ์ž…๋ ฅ ๋ฐ์ดํ„ฐ ํ‘œ์ƒ ๊ตฌ์กฐ 34 ์ œ 4 ์žฅ ์‹คํ—˜ ๋ฐ ํ‰๊ฐ€ 36 4.1 ํ•œ๊ตญ์–ด ๊ตฌ๋ฌถ์Œ 36 4.1.1 ์‹คํ—˜ ํ™˜๊ฒฝ 36 4.1.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 37 4.2 ๊ตฌ๋ฌถ์Œ์„ ๋ฐ˜์˜ํ•œ ํ•œ๊ตญ์–ด ์˜์กด๊ตฌ์กฐ ๋ถ„์„ 39 4.2.1 ์‹คํ—˜ ํ™˜๊ฒฝ 39 4.2.2 ์‹คํ—˜ ๊ฒฐ๊ณผ 42 ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ 47 ์ฐธ๊ณ ๋ฌธํ—Œ 49 ๊ฐ์‚ฌ์˜ ๊ธ€ 59Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธๅคงๅญธๆ ก ่กŒๆ”ฟๅคงๅญธ้™ข :่กŒๆ”ฟๅญธ็ง‘ ่กŒๆ”ฟๅญธๅฐˆๆ”ป,1995.Maste

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    โ… . ์ถ”์ง„๋ฐฐ๊ฒฝ ์ง€๋‚œ 94๋…„, UR์ด ํƒ€๊ฒฐ๋˜๊ณ  WTO์˜ ์‹ ๊ตญ์ œ๊ฒฝ์Ÿ์‚ฌํšŒ๊ฐ€ ์—ด๋ฆฌ๋ฉด์„œ ์šฐ๋ฆฌ ๋†์—…์— ๋Œ€ํ•œ ๊ฑฑ์ •์˜ ์†Œ๋ฆฌ์™€ ์šฐ๋ฆฌ ๋†๋ฏผ๋“ค์˜ ์•ž๋‚ ์— ๋Œ€ํ•œ ๋ถˆ์•ˆ๊ฐ์ด ์ตœ๊ณ ์กฐ์— ๋‹ฌํ–ˆ๋‹ค. ์ตœ์†Œ ์‹œ์žฅ์ ‘๊ทผ์— ์˜ํ•œ ์†Œ๋น„๋Ÿ‰ 1%์˜ ๋ฌผ๋Ÿ‰์ด๋‚˜๋งˆ ์™ธ๊ตญ์Œ€์ด ๊ตญ๋‚ด์—๋„ ๋“ค์–ด์˜ฌ ์ˆ˜ ์žˆ๊ฒŒ ๋˜์—ˆ๋‹ค๋Š” ํ˜‘์ƒ๊ฒฐ๊ณผ๊ฐ€ ์šฐ๋ฆฌ ์ „๊ตญ๋ฏผ์—๊ฒŒ ์ถฉ๊ฒฉ์œผ๋กœ ์™€ ๋‹ฟ์•˜์—ˆ๊ณ  ์šฐ๋ฆฌ ๋†์—…์˜ ์กด๋ฆฝ์— ๋Œ€ํ•œ ์œ„๊ธฐ๊ฐ๋งˆ์ € ๋†๋ฏผ๋“ค์€ ๋ฌผ๋ก  ๋†์ •๊ด€๊ณ„์ž๋“ค์—๊ฒŒ ๊นŒ์ง€ ํŒฝ๋ฐฐํ•ด ์žˆ์—ˆ๋‹ค. -์ดํ›„ ์ƒ๋žต1. ์ถ”์ง„๋ฐฐ๊ฒฝ 2. 1์ฐจ๋…„(96)์˜ ์ถ”์ง„์„ฑ๊ณผ 3. 2์ฐจ๋…„(97) ์ถ”์ง„๊ณ„ํš 4. ์˜ฌํ•ด์˜ ์ค‘์š”ํ•œ ์ •์ฑ…์‚ฌ์—… 5. ๋งบ์Œ

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    1. ์ถœ๋ฐœ์— ์•ž์„œ ์ง€๋‚œํ•ด 11์›” ์šฐ๋ฆฌ ๋†์ดŒ ํ˜„์žฅ์—์„œ๋Š” ํฐ ์†Œ๋ž€์ด ์žˆ์—ˆ๋‹ค. 90๋…„๋งŒ์˜ ๊ฐ€๋ญ„์ด๋ผ๋Š” ๋‚œ๊ด€์„ ๊ทน๋ณตํ•ด๋‚ด๊ณ  ํ’์„ฑํ•œ ๊ฐ€์„ ๋“ค๋…˜์—์„œ ๊ฑฐ๋‘์–ด ๋“ค์ธ ์Œ€์˜ ๊ฐ€๊ฒฉ์ด ์ˆ˜ํ™•์˜ ๊ธฐ์จ์€ ์•„๋ž‘๊ณณํ•˜์ง€ ์•Š๊ณ  ๊ณ„์† ํ•˜๋ฝํ•˜๊ณ  ์žˆ์—ˆ๊ธฐ ๋•Œ๋ฌธ์ด์—ˆ๋‹ค. ๋ฌผ๋ก  ๊ทธํ•ด์˜ ํ’ํ‰์— ๋”ฐ๋ผ ์Œ€๊ฐ’์˜ ๋“ฑ๋ฝ์ด ์žˆ์–ด ์™”์ง€๋งŒ ์ง€๋‚œํ•ด ๋งŒํผ ๊ฐ‘์ž‘์Šค๋Ÿฝ๊ฒŒ ํฐ ํญ์œผ๋กœ ๋–จ์–ด์ง€๋Š” ์˜ˆ๋Š” ์•„์ฃผ ๋“œ๋ฌธ ์ผ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋†์—…์ธ๋“ค์€ ๋ฌผ๋ก ์ด๊ณ  ๋†์ • ๊ณต๋ฌด์›๊ณผ ์ƒ์ธ๋“ค ๋งˆ์ €๋„ ๋งŽ์€ ๊ฑฑ์ •์†์—์„œ ์Œ€๊ฐ’์˜ ์•ˆ์ •์„ ์œ„ํ•˜์—ฌ ๋Œ€์ฑ…์ˆ˜๋ฆฝ์— ๊ณ ์‹ฌํ•˜์˜€๋‹ค. ์šฐ์„  ์ •๋ถ€์ˆ˜๋งค๋ฅผ ์‹ ์†ํ•˜๊ฒŒ ์ถ”์ง„ํ•˜๋Š”๊ฐ€ ํ•˜๋ฉด ์†Œ๋น„์ด‰์ง„ ์šด๋™์„ ํŽผ์น˜๋ฉด์„œ ์šฐ๋ฆฌ ์Œ€์˜ ์ข‹์€ ์ ์„ ์†Œ๋น„์ž์—๊ฒŒ ์•Œ๋ฆฌ๊ณ  ใ€Ž์•„์นจ๋ฐฅ ๋จน๊ธฐ ์บ ํŽ˜์ธใ€์„ ๋ฒŒ์ด๊ธฐ๋„ ํ•˜์˜€๋‹ค. -์ดํ›„ ์ƒ๋žต1. ์ถœ๋ฐœ์— ์•ž์„œ 2. ์œ ๋Ÿฝ์—์„œ์˜ ๋†์—…๊ณผ ๋†์ดŒ ๊ทธ๋ฆฌ๊ณ  ๋†์ •์˜ ์—ญํ•  3. ์œ ๋Ÿฝ ๋†์—…์˜ ์‹œ์‚ฌ
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