정보과학회 논문지 (Journal of KIISE)

  • 제42권2호
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  • Pages.220-226
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  • 2015
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  • 2383-630X(pISSN)
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  • 2383-6296(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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Structural SVM 기반의 한국어 의미역 결정

Korean Semantic Role Labeling Using Structured SVM

  • 투고 : 2014.08.22
  • 심사 : 2014.11.13
  • 발행 : 2015.02.15
⟨ 이전 논문 다음 논문 ⟩

초록

의미역 결정은 자연어 문장의 서술어와 그 서술어에 속하는 논항들 사이의 의미관계를 결정하는 문제이다. 일반적으로 의미역 결정을 위해서는 서술어 인식(Predicate Identification, PI), 서술어 분류(Predicate Classification, PC), 논항 인식(Argument Identification, AI) 논항 분류(Argument Classification, AC) 단계가 수행된다. 본 논문에서는 한국어 의미역 결정 문제를 위해 Korean Propbank를 의미역 결정 학습 말뭉치로 사용하고, 의미역 결정 문제를 Sequence Labeling 문제로 바꾸어 이 문제에서 좋은 성능을 보이는 Structural SVM을 이용하였다. 실험결과 서술어 인식/분류(Predicate Identification and Classification, PIC)에서는 97.13%(F1)의 성능을 보였고, 논항 인식/분류(Argument Identification and Classification, AIC)에서는 76.96%(F1)의 성능을 보였다.

Semantic role labeling (SRL) systems determine the semantic role labels of the arguments of predicates in natural language text. An SRL system usually needs to perform four tasks in sequence: Predicate Identification (PI), Predicate Classification (PC), Argument Identification (AI), and Argument Classification (AC). In this paper, we use the Korean Propbank to develop our Korean semantic role labeling system. We describe our Korean semantic role labeling system that uses sequence labeling with structured Support Vector Machine (SVM). The results of our experiments on the Korean Propbank dataset reveal that our method obtains a 97.13% F1 score on Predicate Identification and Classification (PIC), and a 76.96% F1 score on Argument Identification and Classification (AIC).

키워드

과제정보

연구 과제번호 : 휴먼 지식증강 서비스를 위한 지능진화형 WiseQA 플랫폼 기술 개발

연구 과제 주관 기관 : 정보통신기술진흥센터

참고문헌

  1. Byoung-Soo Kim, Yong-Hun Lee, and Jong-Hyeok Lee, Unsupervised Semantic Role Labeling for Korean Adverbial Case, Journal of KIISE: Software and Applications, Vol. 34, No. 2, 2007.
  2. Martha Palmer, Shijong Ryu, Jinyoung Choi, Sinwon Yoon, and Yeongmi Jeon, Korean Propbank, [Online]. Available: http://catalog.ldc.upenn.edu/LDC2006T03
  3. Hyun-Ki Jung and Yu-Seop Kim, Semantic Role Labeling of Korean Adverbial Arguments by using the Expanded Case Frame Dictionary, Journal of Korean Institute of Information Technology, Vol. 9, No. 10, 2011.
  4. Thorsten Joachims, Thomas Finley, and Chun-Nam John Yu, Cutting-Plane Training of Structural SVMs, Machine Learning, Vol. 77, No. 1, 2009.
  5. Changki Lee, Pum-Mo Ryu, HyunKi Kim, Named Entity Recognition using a modified Pegasos algorithm, CIKM2011, 2011
  6. Changki Lee, Hyunki Kim, Automatic Korean Word Spacing using Pegasos algorithm, Information Processing and Management, Vol. 49, Issue 1, 2013.
  7. Soojong Lim, Changki Lee, and Dongyul Ra, Dependency-based Semantic Role Labeling Using Sequence Labeling with a Structural SVM, in Pattern Recognition Letters, Vol. 34, pp. 696-702, 2013. https://doi.org/10.1016/j.patrec.2013.01.022
  8. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, Efficient Estimation of Word Representations in Vector Space, Proc. of Workshop at ICLR, 2013.
  9. Changki Lee. Joint Models for Korean Word Spacing and POS Tagging using Structural SVM, Journal of KIISE: Software and Applications, Vol. 40, No. 12, 2013.
  10. Changki Lee, Soojong Lim, Myung-Gil Jang, Large-Margin Training of Dependency Parsers Using Pegasos Algorithm, ETRI Journal, Vol. 32, No. 3, 2010.

피인용 문헌

  1. Korean Semantic Role Labeling Using Semantic Frames and Synonym Clusters vol.43, pp.7, 2016, https://doi.org/10.5626/JOK.2016.43.7.773
  2. Korean Semantic Role Labeling Using Case Frame Dictionary and Subcategorization vol.43, pp.12, 2016, https://doi.org/10.5626/JOK.2016.43.12.1376