Feature-Based Relation Classification Using Quantified Relatedness Information

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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 chi-square 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.
Publisher
ELECTRONICS TELECOMMUNICATIONS RESEARCH INST
Issue Date
2010-06
Language
English
Article Type
Article
Keywords

TEXT

Citation

ETRI JOURNAL, v.32, no.3, pp.482 - 485

ISSN
1225-6463
DOI
10.4218/etrij.10.0209.0353
URI
http://hdl.handle.net/10203/95196
Appears in Collection
CS-Journal Papers(저널논문)
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