A Multi-view Approach for Relation Extraction

Authors:
Junsheng Zhou;Qian Xu;Jiajun Chen;Weiguang Qu
Affiliations:
Department of Computer Science and technology, Nanjing Normal University, Nanjing, China and Jiangsu Research Center of Information Security & Confidential Engineering, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Department of Computer Science and technology, Nanjing Normal University, Nanjing, China and Jiangsu Research Center of Information Security & Confidential Engineering, Nanjing, China
Venue:
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Year:
2009

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Abstract

Relation extraction is an important problem in information extraction. In this paper, we explore a multi-view strategy for relation extracting task. Motivated by the fact, as in work of Jiang and Zhai's [1], that combining different feature subspaces into a single view does not generate much improvement, we propose a two-stage multi-view learning approach. First, we learn two different classifiers from two different views of relation instances: sequence representation and syntactic parse tree representation, respectively. Then, a meta-learner is trained using the meta data constructed along with other contextual information to achieve a strong predictive performance, as the final classification model. The experimental results conducted on ACE 2005 corpus show that the multi-view approach outperforms each single-view one for relation extraction task.