The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Two-Phase Shallow Semantic Parsing based on Partial Syntactic Parsing

부분 구문 분석 결과에 기반한 두 단계 부분 의미 분석 시스템

  • 박경미 (숭실대학교 정보미디어기술연구소) ;
  • 문영성 (숭실대학교 컴퓨터학부)
  • Published : 2010.02.28
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Abstract

A shallow semantic parsing system analyzes the relationship that a syntactic constituent of the sentence has with a predicate. It identifies semantic arguments representing agent, patient, instrument, etc. of the predicate. In this study, we propose a two-phase shallow semantic parsing model which consists of the identification phase and the classification phase. We first find the boundary of semantic arguments from partial syntactic parsing results, and then assign appropriate semantic roles to the identified semantic arguments. By taking the sequential two-phase approach, we can alleviate the unbalanced class distribution problem, and select the features appropriate for each task. Experiments show the relative contribution of each phase on the test data.

부분 의미 분석 시스템은 문장의 구성 요소들이 술어와 갖는 관계를 분석하는 것으로 문장에서 술어의 주체, 객체, 도구 등을 나타내는 의미 논항을 확인하게 된다. 본 논문에서 개발한 부분 의미 분석 시스템은 두 단계로 구성되어 있는데, 먼저 부분 구문 분석 결과로부터 의미 논항의 경계를 찾는 의미 논항 확인 단계를 수행하고 다음으로 확인된 의미 논항에 적절한 의미역을 부착하는 의미역 할당 단계를 수행한다. 순차적인 두 단계 방법을 적용하는 것에 의해서, 학습 성능 저하의 주요한 원인인 클래스 분포의 불균형 문제를 완화할 수 있고, 각 단계에 적합한 자질을 선별하여 사용할 수 있다. 본 논문에서는 PropBank 말뭉치에 기반한 CoNLL-2004 shared task의 데이터 집합 및 평가 프로그램을 사용하여 각 단계가 시스템의 전체 성능에 기여하는 정도를 보인다.

Keywords

References

  1. Buchholz, S., Memory-Based Grammatical Relation Finding, PhD. thesis, Tilburg University, 2002.
  2. Carreras, X. and Marquez, L., Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling, In Proceedings of the Eighth Conference on Natural Language Learning, 2004.
  3. CoNLL shared task, http://www.lsi.upc.edu/~srlconll/home.html
  4. Gildea, D. and Jurafsky, D., Automatic Labeling of Semantic Roles, Computational Linguistics, Vol. 28, No. 3, pp. 1-45, 2002. https://doi.org/10.1162/089120102317341747
  5. Hacioglu, K., Pradhan, S., Ward, W., Martin, J. and Jurafsky, D., Semantic Role Labeling by Tagging Syntactic Chunks, In Proceedings of the Eighth Conference on Natural Language Learning, 2004.
  6. Kwon, N., Fleischman, M. and Hovy, E., FrameNet-based semantic parsing using maximum entropy models, In Proceedings of the International Conference on Computational Linguistics, 2004. https://doi.org/10.3115/1220355.1220534
  7. Pradhan, S., Hacioglu, K., Krugler, V., Ward, W., Martin, J. and Jurafsky, D., Shallow Semantic Parsing Using Support Vector Machines, Technical Report, TR-CSLR-2003-03, 2003.
  8. Pradhan, S., Ward, W., Hacioglu, K., Martin, J. and Jurafsky. D., Semantic Role Labeling using Different Syntactic Views, In Proceedings of the Association for Computational Linguistics, 2005. https://doi.org/10.3115/1219840.1219912
  9. Punyakanok, V., Roth, D., Yih, W., Zimak, D. and Tu, Y., Semantic Role Labeling Via Generalized Inference Over Classifiers, In Proceedings of the Eighth Conference on Natural Language Learning, 2004.
  10. SupportVector Machine(SVM)-Light, http://svmlight.joachims.or
  11. Surdeanu, M., Harabagiu, S., Williams, J. and Aarseth, P., Using Predicate Arguments Structures for Information Extraction, In Proceedings of the Association for Computational Linguistics, 2003. https://doi.org/10.3115/1075096.1075098
  12. Xue, N. and Palmer, M., Calibrating Features for Semantic Role Labeling, In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2004.
  13. Yamada, H. and Matsumoto, Y., Statistical Dependency Analysis with Support Vector Machines, In Proceedings of the 8th International Workshop of Parsing Technologies, 2003.

Cited by

  1. Analysis of Sentential Paraphrase Patterns and Errors through Predicate-Argument Tuple-based Approximate Alignment vol.19B, pp.2, 2012, https://doi.org/10.3745/KIPSTB.2012.19B.2.135