도움말

A Korean Morphological Analyzer using a Pre-analyzed Partial Word-phrase Dictionary

Vol.39 No.5, 2012.5, 415-424 (10 pages)
Copy
Quick View Quick View
Purchase $5.29
Export
Usage : 750
Citations : 1
분야내 활용도 : 1%
More detail >

· Usage : Full-text article downdloads count since 2010.

· Citations : Cited in the DBpia's articles

· Impact Score : Calculates the article impact score on a basis of the usage in the last 24 months.

Abstract
말뭉치 기반 한국어 형태소 분석 방법은 대용량의 기분석 어절사전을 사용하여 분석하고, 그 사전에 없는 어절의 경우 코드 변환, 형태소 분리, 원형 복원 등의 복잡한 분석 규칙을 통해 후보들을 생성했다. 이 복잡한 분석 규칙은 프로그램의 제작과 유지보수, 실행 관점 모두에서 효율적이지 못하며 정확률을 떨어뜨리고 속도를 느리게 하는 요인이 된다. 이런 문제를 해결하기 위해 기분석 부분 어절 사전을 구축하여 사용하는 방법이 연구되었다.
본 논문에서는 대용량의 분석 말뭉치를 통해 기분석 부분 어절 사전을 구축하고 형태소 분석에 사용하는 방법을 제안한다. 세종 말뭉치로 실험한 결과 형태소 분석의 재현율이 99.05%였으며, 은닉 마르코프 모델을 이용한 품사 및 동형이의어 태깅 정확률은 96.76%였다.

The Korean morphological analysis based on corpus usually uses the pre-analyzed full word-phrase dictionary(FWD) that is constructed from the corpus. If input words are not found in the FWD, the morphemes of the input words are analyzed using complicated analysis rules: code transformation, decomposition of morphemes, and restoration of original form. Such complicated analysis rules are inefficient in terms of programming, maintenance, and runtime and cause to reduce its accuracy and performance. In order to solve these problems, the method using a pre-analyzed partial word-phrase dictionary(PWD) was researched.
This paper proposes new method that constructs the PWD from tagged corpus and analyzes Korean morpheme using the PWD. According to the experiments on Sejong corpus, the recall of morpheme analysis is 99.05%. And the accuracy of POS with homonym tagging based on Hidden-Markov-Model is 96.76%.

TOC
요약
Abstract
1. 서론
2. 학습 데이터 구축
3. 형태소 분석 및 태깅
4. 실험과 결과
5. 사용자 사전
6. 결론
참고문헌
Keyword
References (11)

Please found references of this article.

  1. Seung Hyun Yang , 2003 , A High-Speed Korean Morphological Analysis Method based on Pre-Analyzed Partial Words , Journal of KIISE. Software and Applications 27 (3) : 290 ~ 301

  2. Seong-Yong Kim , 1987 , A morphological analyzer for korean language with tabular parsing method and connectivity information , 석사 , KAIST Computer Science

  3. J. H. Choi , 1993 , A Method for Reducing Dictionary Access with Bidirectional Longest Match Strategy in Korean Morphological Analyzer , Journal of KIISE 20 (1) : 769 ~ 772

  4. Y. G. Han , 1999 , Pseudo two-level model using extended longest match method in korean morphological analysis , Proceedings of Conference of Hangul and Korean Information Processing : 491 ~ 496

  5. J. H. Choi , 1993 , A Method for Reducing Dictionary Access with Bidirectional Longest Match Strategy in Korean Morphological Analyzer , Journal of KIISE 20 (10) : 1497 ~ 1507

  6. Seung-Shik Kang , 1991 , A design and implementation of efficient Korean morphological analyzer based on dictionary information , Proceedings of KIISE Spring Conference 18 (1)

  7. Jae-han Kim , 1994 , Korean Morphorlogical Analyzer using Unified-Morphem Information , Proceedings of KIISE Fall Conference 21 (2) : 653 ~ 656

  8. 심광섭 , 2007 , MADE : 형태소 분석기 개발 환경 , 인터넷정보학회논문지 8 (4) : 159 ~ 171

  9. Jung-ho Shin , 1994 , An HMM Part-of-Speech Tagger for Korean Based on Wordphrase , Proceedings of Conference of Hangul and Korean Information Processing : 389 ~ 394

  10. Dong Myung Kim , 2009 , Simultaneous Korean POS and Homonym Tagging System using HMM , 석사 , Ulsan University

  • 처음
  •  
  • 이전
  •  
  • 1
  •  
  • 2
  •  
  • 다음
  •  
  • 마지막
Cited articles (1)

Please apply for Alerts and check the information by e-mail!

Other articles of first author (10)

Please check the detailed of Joon-Choul Shin Identified author!

Within this Journal (9)

Please check the detailed of Vol.39 No.5!

Recommended Articles (10)

We provide services, 'DBpia Recommended Articles' and 'Customers who used this article also used', that used text mining, usage and citations data.

DBpia Recommended Articles

More recommended articles!

Customers who used this article also used

Metrics

Usage Status

· Usage

· Top 3 institutions list on usage

More detail >
No Top institutions Usage
1 연세대학교 45
2 충북대학교 41
3 울산대학교 36

Impact Score

· Impact Score

· The article impact score on the subject

More detail >

: %

2016-09
2016-10
2016-11
2016-12
0
20
40
60
80
100
  • 0%
  • 20%
  • 40%
  • 60%
  • 80%
  • 100%

Citations

Detailed Info
Copyright Policy

The copyright of all work are belongs to the original author. The contents of each work shall not be responsible or guarantee. Crawl the metadata of articles do not allowed without agreement.

top