Open AccessFeature‐Based Relation Classification Using Quantified Relatedness Information
Author(s) -
Huang JinXia,
Choi KeySun,
Kim ChangHyun,
Kim YoungKil
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.0209.0353
Subject(s) - feature (linguistics) , relation (database) , feature selection , artificial intelligence , computer science , set (abstract data type) , pattern recognition (psychology) , probabilistic logic , natural language processing , data mining , linguistics , philosophy , programming language
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.