Detecting semantic relations between named entities in text using contextual features

Authors:
Toru Hirano;Yoshihiro Matsuo;Genichiro Kikui
Affiliations:
NTT Corporation, Yokosuka-Shi, Kanagawa, Japan;NTT Corporation, Yokosuka-Shi, Kanagawa, Japan;NTT Corporation, Yokosuka-Shi, Kanagawa, Japan
Venue:
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Year:
2007

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Abstract

This paper proposes a supervised learning method for detecting a semantic relation between a given pair of named entities, which may be located in different sentences. The method employs newly introduced contextual features based on centering theory as well as conventional syntactic and word-based features. These features are organized as a tree structure and are fed into a boosting-based classification algorithm. Experimental results show the proposed method outperformed prior methods, and increased precision and recall by 4.4% and 6.7%.