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학술지 Feature-Based Relation Classification Using Quantified Relatedness Information
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저자
황금하, 최기선, 김창현, 김영길
발행일
201006
출처
ETRI Journal, v.32 no.3, pp.482-485
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.10.0209.0353
협약과제
09MS5200, 한중영 대화체 및 기업문서 자동번역 기술개발, 김영길
초록
Feature selection is very important for feature-based relation classification tasks. While most of the existing works on feature selection rely on linguistic information acquired using parsers, this letter proposes new features, including probabilistic and semantic relatedness features, to manifest the relatedness between patterns and certain relation types in an explicit way. The impact of each feature set is evaluated using both a chisquare estimator and a performance evaluation. The experiments show that the impact of relatedness features is superior to existing well-known linguistic features, and the contribution of relatedness features cannot be substituted using other normally used linguistic feature sets.
키워드
Feature selection, Feature-based, Probabilistic relatedness, Relation classification, Semantic relatedness
KSP 제안 키워드
Feature selection(FS), Feature set, Feature-based, Performance evaluation, linguistic features, linguistic information, relation classification, semantic relatedness