Acquisition of Semantic Lexicons: Using Word Sense Disambiguation to Improve Precision

Acquisition of Semantic Lexicons: Using Word Sense Disambiguation to Improve Precision Bonnie J. Dorr and Doug Jones Department of Computer Science and Institute for Advanced Computer Studies University of Maryland A. V. Williams Building College Park, MD 20742 {bonnie,jones } @umiacs.umd.edu Abstract This paper addresses the problem of large-scale acquisition of computational-semantic lexicons from machine-readable resources. We describe semantic filters designed to reduce the number of incorrect assignments (i.e., improve precision) made by a purely syntactic technique. We demonstrate that it is possible to use these filters to build broad-coverage lexicons with minimal effort, at a depth of knowledge that lies at the syntax-semantics interface. We report on our results of disambiguating the verbs in the semantic filters by adding WordNet 1 sense annotations. We then show the results of our classification on unknown words and we evaluate these results. 1 Introduction This paper addresses the problem of large-scale acquisition of computational-semantic lexicons from machine-readable resources. We describe semantic filters designed to reduce the number of incorrect assignments (i.e., improve precision) made by a purely syntactic technique. We demonstrate that it is possible to use these filters to build broad-coverage lexicons with minimal effort, at a depth of knowledge that lies at the syntax-semantics interface. We report on our results of disambiguating the verbs in the semantic filters by adding WordNet sense annotations. We then show the results of our classification on unknown words and we evaluate these results. As machine-readable resources (i.e., online dictionaries, thesauri, and other knowledge sources) become readily available to NLP researchers, automated acquisition has become increasingly more attractive. Several researchers have noted that the average time needed to construct a lexical entry by hand can be as much as 30 minutes (see, e.g., (Neff and McCord, 1990; Copestake et al., 1995; Walker and Amsler, 1986)). Given that most large-scale NLP applications require lexicons of 20-60,000 words, automation of the acquisition process has become a necessity. Previous research in automatic acquisition focuses primarily on the use of statistical techniques, such as bilingual alignment (Church and Hanks, 1990; Klavans and Tzoukermann, 1996; Wu and Xia, 1995), or extraction of syntactic constructions from online dictionaries and corpora (Brent, 1993; Dorr, Garman, and Weinberg, 1995). Others who have taken a more knowledge-based (in- terlingual) approach (Lonsdale, Mitamura, and Nyberg, 1996) do not provide a means for system- atically deriving the relation between surface syntactic structures and their underlying semantic representations. Those who have taken more argument structures into account, e.g., (Copestake et al., 1995), do not take full advantage of the systematic relation between syntax and semantics during lexical acquisition. 1We used Version 1.5 of WordNet, available at http://www.cogsci.princeton.edu/~wn. 42 ! We adopt the central thesis of Levin (1993), i.e., that the semantic class of a verb and its syntactic behavior are predictably related. We base our work on a correlation between semantic classes and patterns of grammar codes in the Longman's Dictionary of Contemporary English (LDOCE) (Procter, 1978). We extend this work by coupling the syntax-semantics relation with …

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