Direct Word Sense Matching for Lexical Substitution

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 449–456, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Direct Word Sense Matching for Lexical Substitution Ido Dagan1, Oren Glickman1, Alfio Gliozzo2, Efrat Marmorshtein1, Carlo Strapparava2 1Department of Computer Science, Bar Ilan University, Ramat Gan, 52900, Israel 2ITC-Irst, via Sommarive, I-38050, Trento, Italy Abstract This paper investigates conceptually and empirically the novel sense matching task, which requires to recognize whether the senses of two synonymous words match in context. We suggest direct approaches to the problem, which avoid the intermediate step of explicit word sense disambigua- tion, and demonstrate their appealing ad- vantages and stimulating potential for fu- ture research. 1 Introduction In many language processing settings it is needed to recognize that a given word or term may be sub- stituted by a synonymous one. In a typical in- formation seeking scenario, an information need is specified by some given source words. When looking for texts that match the specified need the source words might be substituted with synony- mous target words. For example, given the source word ‘weapon’ a system may substitute it with the target synonym ‘arm’. This scenario, which is generally referred here as lexical substitution, is a common technique for increasing recall in Natural Language Process- ing (NLP) applications. In Information Retrieval (IR) and Question Answering (QA) it is typically termed query/question expansion (Moldovan and Mihalcea, 2000; Negri, 2004). Lexical Substi- tution is also commonly applied to identify syn- onyms in text summarization, for paraphrasing in text generation, or is integrated into the features of supervised tasks such as Text Categorization and Information Extraction. Naturally, lexical substi- tution is a very common first step in textual en- tailment recognition, which models semantic in- ference between a pair of texts in a generalized ap- plication independent setting (Dagan et al., 2005). To perform lexical substitution NLP applica- tions typically utilize a knowledge source of syn- onymous word pairs. The most commonly used resource for lexical substitution is the manually constructed WordNet (Fellbaum, 1998). Another option is to use statistical word similarities, such as in the database constructed by Dekang Lin (Lin, 1998). We generically refer to such resources as substitution lexicons. When using a substitution lexicon it is assumed that there are some contexts in which the given synonymous words share the same meaning. Yet, due to polysemy, it is needed to verify that the senses of the two words do indeed match in a given context. For example, there are contexts in which the source word ‘weapon’ may be substituted by the target word ‘arm’; however one should recog- nize that ‘arm’ has a different sense than ‘weapon’ in sentences such as “repetitive movements could cause injuries to hands, wrists and arms.” A commonly proposed approach to address sense matching in lexical substitution is applying Word Sense Disambiguation (WSD) to identify the senses of the source and target words. Then, substitution is applied only if the words have …

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