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Detail
Journal Article
Feature-Based Relation Classification Using Quantified Relatedness Information
- Authors
- Jin-Xia Huang, Key-Sun Choi, Chang-Hyun Kim, Young-Kil Kim
- Issue Date
- 2010-06
- Citation
- ETRI Journal, v.32, no.3, pp.482-485
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Feature-based, Linguistic features, Linguistic information, Performance evaluation, feature selection, feature set, relation classification, semantic relatedness