한국정보과학회논문지:소프트웨어및응용 (Journal of KIISE:Software and Applications)

한국정보과학회 (Korean Institute of Information Scientists and Engineers)
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최대 엔트로피 부스팅 모델을 이용한 영어 전치사구 접속과 품사 결정 모호성 해소

(Resolving Prepositional Phrase Attachment and POS Tagging Ambiguities using a Maximum Entropy Boosting Model)

  • 박성배 (서울대학교 컴퓨터신기술공동연구소)
  • 발행 : 2003.06.01
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초록

최대 엔트로피 모델은 자연언어를 모델링하기 위한 좋은 방법이다. 하지만, 최대 엔트로피 모델을 전치사구 접속과 같은 실제 언어 문제에 적용할 때, 자질 선택과 계산 복잡도의 두 가지 문제가 발생한다. 본 논문에서는, 이런 문제와 자연언어 자원에 존재하는 불균형 데이터 문제를 해결하기 위한 최대 엔트로피 부스팅 모델(maximum entropy boosting model)을 제시하고, 이를 영어의 전치사구 접속과 품사 결정 모호성 해소에 적용한다. Wall Street Journal 말뭉치에 대한 실험 결과, 문제의 모델링에 아주 작은 노력을 들였음에도 불구하고, 전치사구 접속 문제에 대해 84.3%의 정확도와 품사 결정 문제에 대해 96.78%의 정확도를 보여 지금까지 알려진 최고의 성능과 비슷한 결과를 보였다.

Maximum entropy models are promising candidates for natural language modeling. However, there are two major hurdles in applying maximum entropy models to real-life language problems, such as prepositional phrase attachment: feature selection and high computational complexity. In this paper, we propose a maximum entropy boosting model to overcome these limitations and the problem of imbalanced data in natural language resources, and apply it to prepositional phrase (PP) attachment and part-of-speech (POS) tagging. According to the experimental results on Wall Street Journal corpus, the model shows 84.3% of accuracy for PP attachment and 96.78% of accuracy for POS tagging that are close to the state-of-the-art performance of these tasks only with small efforts of modeling.

키워드

참고문헌

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