Learning to Extract Relations from the Web using Minimal Supervision

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 576–583, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics Learning to Extract Relations from the Web using Minimal Supervision Razvan C. Bunescu Department of Computer Sciences University of Texas at Austin 1 University Station C0500 Austin, TX 78712 razvan@cs.utexas.edu Raymond J. Mooney Department of Computer Sciences University of Texas at Austin 1 University Station C0500 Austin, TX 78712 mooney@cs.utexas.edu Abstract We present a new approach to relation ex- traction that requires only a handful of train- ing examples. Given a few pairs of named entities known to exhibit or not exhibit a particular relation, bags of sentences con- taining the pairs are extracted from the web. We extend an existing relation extraction method to handle this weaker form of su- pervision, and present experimental results demonstrating that our approach can reliably extract relations from web documents. 1 Introduction A growing body of recent work in information extraction has addressed the problem of relation extraction (RE), identifying relationships between entities stated in text, such as LivesIn(Person, Location) or EmployedBy(Person, Company). Supervised learning has been shown to be effective for RE (Zelenko et al., 2003; Culotta and Sorensen, 2004; Bunescu and Mooney, 2006); however, anno- tating large corpora with examples of the relations to be extracted is expensive and tedious. In this paper, we introduce a supervised learning approach to RE that requires only a handful of training examples and uses the web as a corpus. Given a few pairs of well-known entities that clearly exhibit or do not exhibit a particular re- lation, such as CorpAcquired(Google, YouTube) and not(CorpAcquired(Yahoo, Microsoft)), search engine is used to find sentences on the weba that mention both of the entities in each of the pairs. Although not all of the sentences for positive pairs will state the desired relationship, many of them will. Presumably, none of the sentences for negative pairs state the targeted relation. Multiple instance learning (MIL) is a machine learning framework that exploits this sort of weak supervision, in which a positive bag is a set of instances which is guaranteed to contain at least one positive example, and a negative bag is a set of instances all of which are negative. MIL was originally introduced to solve a problem in biochemistry (Dietterich et al., 1997); however, it has since been applied to problems in other areas such as classifying image regions in computer vision (Zhang et al., 2002), and text categorization (Andrews et al., 2003; Ray and Craven, 2005). We have extended an existing approach to rela- tion extraction using support vector machines and string kernels (Bunescu and Mooney, 2006) to han- dle this weaker form of MIL supervision. This ap- proach can sometimes be misled by textual features correlated with the specific entities in the few train- ing pairs provided. Therefore, we also describe a method for weighting features in order to focus on those correlated with the target relation rather than with the individual …

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