Mapping Lexical Entries in a Verbs Database to WordNet Senses

Mapping Lexical Entries in a Verbs Database to WordNet Senses Rebecca Green � and Lisa Pearl � and Bonnie J. Dorr �� and Philip Resnik �� � Institute for Advanced Computer Studies � Department of Computer Science University of Maryland College Park, MD 20742 USA � rgreen,llsp,bonnie,resnik � @umiacs.umd.edu Abstract This paper describes automatic tech- niques for mapping 9611 entries in a database of English verbs to Word- Net senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval. Our techniques make use of (1) a training set of 1791 disambiguated entries, representing 1442 verb entries from 167 classes; (2) word sense probabilities, from frequency counts in a tagged corpus; (3) semantic similarity of WordNet senses for verbs within the same class; (4) probabilistic correlations between WordNet data and attributes of the verb classes. The best results achieved 72% precision and 58% recall, versus a lower bound of 62% precision and 38% recall for assigning the most frequently occurring WordNet sense, and an upper bound of 87% precision and 75% recall for human judgment. 1 Introduction Our goal is to map entries in a lexical database of 4076 English verbs automatically to Word- Net senses (Miller and Fellbaum, 1991), (Fell- baum, 1998) to support such applications as ma- chine translation and cross-language information retrieval. For example, the verb drop is multi- ply ambiguous, with many potential translations in Spanish: bajar, caerse, dejar caer, derribar, disminuir,echar, hundir, soltar,etc. The database specifies a set of interpretations for drop, depend- ing on its context in the source-language (SL). In- clusion of WordNet senses in the database enables the selection of an appropriate verb in the target language (TL). Final selection is based on a fre- quency count of WordNet senses across all classes to which the verb belongs—e.g., disminuir is se- lected when the WordNet sense corresponds to the meaning of drop in Prices dropped. Our task differs from standard word sense dis- ambiguation (WSD) in several ways. First, the words to be disambiguated are entries in a lexical database, not tokens in a text corpus. Second, we take an “all-words” rather than a “lexical-sample” approach (Kilgarriff and Rosenzweig, 2000): All words in the lexical database “text” are disam- biguated, not just a small number for which de- tailed knowledge is available. Third, we replace the contextual data typically used for WSD with information about verb senses encoded in terms of thematic grids and lexical-semantic representa- tions from (Olsen et al., 1997). Fourth, whereas a single word sense for each token in a text corpus is often assumed, the absence of sentential context leads to a situation where several WordNet senses may be equally appropriate for a database entry. Indeed, as distinctions between WordNet senses can be fine-grained (Palmer, 2000), it may be un- clear, even in context, which sense is meant. The verb database contains mostly …

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