Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 1021–1029, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web Yulan Yan, Naoaki Okazaki, Yutaka Matsuo, Zhenglu Yang and Mitsuru Ishizuka The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan yulan@mi.ci.i.u-tokyo.ac.jp okazaki@is.s.u-tokyo.ac.jp matsuo@biz-model.t.utokyo.ac.jp yangzl@tkl.iis.u-tokyo.ac.jp ishizuka@i.u-tokyo.ac.jp Abstract This paper presents an unsupervised rela- tion extraction method for discovering and enhancing relations in which a specified concept in Wikipedia participates. Using respective characteristics of Wikipedia ar- ticles and Web corpus, we develop a clus- tering approach based on combinations of patterns: dependency patterns from depen- dency analysis of texts in Wikipedia, and surface patterns generated from highly re- dundant information related to the Web. Evaluations of the proposed approach on two different domains demonstrate the su- periority 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. 1 Introduction Machine learning approaches for relation extrac- tion tasks require substantial human effort, partic- ularly when applied to the broad range of docu- ments, entities, and relations existing on the Web. Even with semi-supervised approaches, which use a large unlabeled corpus, manual construction of a small set of seeds known as true instances of the target entity or relation is susceptible to arbitrary human decisions. Consequently, a need exists for development of semantic information-retrieval al- gorithms that can operate in a manner that is as unsupervised as possible. Currently, the leading methods in unsupervised information extraction collect redundancy infor- mation from a local corpus or use the Web as a corpus (Pantel and Pennacchiotti, 2006); (Banko et al., 2007); (Bollegala et al., 2007): (Fan et al., 2008); (Davidov and Rappoport, 2008). The standard process is to scan or search the cor- pus to collect co-occurrences of word pairs with strings between them, and then to calculate term co-occurrence or generate surface patterns. The method is used widely. However, even when pat- terns are generated from well-written texts, fre- quent pattern mining is non-trivial because the number of unique patterns is loose, but many pat- terns are non-discriminative and correlated. salient challenge and research interest for frequentA pattern mining is abstraction away from different surface realizations of semantic relations to dis- cover discriminative patterns efficiently. Linguistic analysis is another effective tech- nology for semantic relation extraction, as de- scribed in many reports such as (Kambhatla, 2004); (Bunescu and Mooney, 2005); (Harabagiu et al., 2005); (Nguyen et al., 2007). Currently, lin- guistic approaches for semantic relation extraction are mostly supervised, relying on pre-specification of the desired relation or initial seed words or pat- terns from hand-coding. The common process is to generate linguistic features based on analyses of the syntactic features, dependency, or shallow se- mantic structure of text. Then the system is trained to identify entity pairs that assume a relation and to classify them into pre-defined relations. …

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