Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Korean Semantic Role Labeling Using Structured SVM

Structural SVM 기반의 한국어 의미역 결정

  • Received : 2014.08.22
  • Accepted : 2014.11.13
  • Published : 2015.02.15
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Abstract

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).

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.

Keywords

Acknowledgement

Grant : 휴먼 지식증강 서비스를 위한 지능진화형 WiseQA 플랫폼 기술 개발

Supported by : 정보통신기술진흥센터

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Cited by

  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