An Empirical Research on Extracting Relations from Wikipedia Text

Authors:
Jin-Xia Huang;Pum-Mo Ryu;Key-Sun Choi
Affiliations:
SWRC, Computer Science Division, EECS Dept. KAIST, Daejeon, Republic of Korea 305-701;SWRC, Computer Science Division, EECS Dept. KAIST, Daejeon, Republic of Korea 305-701;SWRC, Computer Science Division, EECS Dept. KAIST, Daejeon, Republic of Korea 305-701
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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Abstract

A feature based relation classification approach is presented, in which probabilistic and semantic relatedness features between patterns and relation types are employed with other linguistic information. The importance of each feature set is evaluated with Chi-square estimator, and the experiments show that, the relatedness features have big impact on the relation classification performance. A series experiments are also performed to evaluate the different machine learning approaches on relation classification, among which Bayesian outperformed other approaches including Support Vector Machine (SVM).