Unsupervised relation extraction by mining Wikipedia texts using information from the web

Authors:
Yulan Yan;Naoaki Okazaki;Yutaka Matsuo;Zhenglu Yang;Mitsuru Ishizuka
Affiliations:
The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan
Venue:
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Year:
2009

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Abstract

This paper presents an unsupervised relation extraction method for discovering and enhancing relations in which a specified concept in Wikipedia participates. Using respective characteristics of Wikipedia articles and Web corpus, we develop a clustering approach based on combinations of patterns: dependency patterns from dependency analysis of texts in Wikipedia, and surface patterns generated from highly redundant information related to the Web. Evaluations of the proposed approach on two different domains demonstrate the superiority of the pattern combination over existing approaches. Fundamentally, our method demonstrates how deep linguistic patterns contribute complementarily with Web surface patterns to the generation of various relations.