Extracting Lexical Reference Rules from Wikipedia

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 450–458, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Extracting Lexical Reference Rules from Wikipedia Eyal Shnarch Computer Science Department Bar-Ilan University Ramat-Gan 52900, Israel shey@cs.biu.ac.il Libby Barak Dept. of Computer Science University of Toronto Toronto, Canada M5S 1A4 libbyb@cs.toronto.edu Ido Dagan Computer Science Department Bar-Ilan University Ramat-Gan 52900, Israel dagan@cs.biu.ac.il Abstract This paper describes the extraction from Wikipedia of lexical reference rules, iden- tifying references to term meanings trig- gered by other terms. We present extrac- tion methods geared to cover the broad range of the lexical reference relation and analyze them extensively. Most extrac- tion methods yield high precision levels, and our rule-base is shown to perform bet- ter than other automatically constructed baselines in a couple of lexical expan- sion and matching tasks. Our rule-base yields comparable performance to Word- Net while providing largely complemen- tary information. 1 Introduction A most common need in applied semantic infer- ence is to infer the meaning of a target term from other terms in a text. For example, a Question An- swering system may infer the answer to a ques- tion regarding luxury cars from a text mentioning Bentley, which provides a concrete reference to the sought meaning. Aiming to capture such lexical inferences we followed (Glickman et al., 2006), which coined the term lexical reference (LR) to denote refer- ences in text to the specific meaning of a target term. They further analyzed the dataset of the First Recognizing Textual Entailment Challenge (Da- gan et al., 2006), which includes examples drawn from seven different application scenarios. It was found that an entailing text indeed includes a con- crete reference to practically every term in the en- tailed (inferred) sentence. The lexical reference relation between two terms may be viewed as a lexical inference rule, denoted LHS ⇒ RHS. Such rule indicates that the left-hand-side term would generate a reference, in some texts, to a possible meaning of the right hand side term, as the Bentley ⇒ luxury car example. In the above example the LHS is a hyponym of the RHS. Indeed, the commonly used hyponymy, synonymy and some cases of the meronymy rela- tions are special cases of lexical reference. How- ever, lexical reference is a broader relation. For instance, the LR rule physician ⇒ medicine may be useful to infer the topic medicine in a text cate- gorization setting, while an information extraction system may utilize the rule Margaret Thatcher ⇒ United Kingdom to infer a UK announcement from the text “Margaret Thatcher announced”. To perform such inferences, systems need large scale knowledge bases of LR rules. A prominent available resource is WordNet (Fellbaum, 1998), from which classical relations such as synonyms, hyponyms and some cases of meronyms may be used as LR rules. An extension to WordNet was presented by (Snow et al., 2006). Yet, available resources do not cover the full scope of lexical ref- erence. This …

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