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

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Identification of Maximal-Length Noun Phrases Based on Expanded Chunks and Classified Punctuations in Chinese

확장청크와 세분화된 문장부호에 기반한 중국어 최장명사구 식별

  • 백설매 ((주)4U Applications) ;
  • 이금희 (포항공과대학교 컴퓨터공학과) ;
  • 김동일 (연변과학기술대학교 컴퓨터전자통신학부) ;
  • 이종혁 (포항공과대학교 컴퓨터공학과)
  • Published : 2009.04.15
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Abstract

In general, there are two types of noun phrases(NP): Base Noun Phrase(BNP), and Maximal-Length Noun Phrase(MNP). MNP identification can largely reduce the complexity of full parsing, help analyze the general structure of complex sentences, and provide important clues for detecting main predicates in Chinese sentences. In this paper, we propose a 2-phase hybrid approach for MNP identification which adopts salient features such as expanded chunks and classified punctuations to improve performance. Experimental result shows a high quality performance of 89.66% in $F_1$-measure.

일반적으로 명사구는 기본명사구와 최장명사구로 분류되는데 최장명사구에 대한 정확한 식별은 문장의 전체적인 구문구조를 파악하고 정확한 지배용언을 찾아내는데 중요한 역할을 하게 된다. 본 논문에서는 확장된 청크(chunk) 개념과 다섯 개의 클래스로 세분화된 문장부호 정보를 자질로 사용한 두 단계 최장명사구 식별 기법을 제안한다. 제안한 기법은 기본모델보다 2.65% 향상된 평균 89.66%($F_1$-measure)의 우수한 성능을 보인다.

Keywords

References

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