Role of Word Sense Disalnbiguation in Lexical Acquisition: Predicting Semantics from Syntactic Cues

Role of Word Sense Disambiguation in Lexical Acquisition: Predicting Semantics from Syntactic Cues 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, j ones}~umiacs, umd. edu ABSTRACT This paper addresses the issue of word-sense ambiguity in extraction from machine-readable resources for the con- struction of large-scale knowledge sources. We describe two experiments: one which ignored word-sense distinctions, re- sulting in 6.3% accuracy for semantic classification of verbs based on (Levin, 1993); and one which exploited word-sense distinctions, resulting in 97.9% accuracy. These experiments were dual purpose: (1) to validate the central thesis of the work of (Levin, 1993), i.e., that verb semantics and syntactic behavior are predictably related; (2) to demonstrate that a 15-fold improvement can be achieved in deriving semantic information from syntactic cues if we first divide the syntac- tic cues into distinct groupings that correlate with different word senses. Finally, we show that we can provide effective acquisition techniques for novel word senses using a combi- nation of online sources. 1 Introduction This paper addresses the issue of word-sense ambigu- ity in extraction from machine-readable resources for the construction of large-scale knowledge sources. We describe two experiments: one which ignored word- sense distinctions, resulting in 6.3% accuracy for seman- tic classification of verbs based on (Levin, 1993); and one which exploited word-sense distinctions, resulting in 97.9% accuracy. These experiments were dual pur- pose: (l) to validate the central thesis of the work of (Levin, 1993), i.e., that verb semantics and syntactic be- havior are predictably related; (2) to demonstrate that a 15-fold improvement can be achieved in deriving se- mantic information from syntactic cues if we first divide the syntactic cues into distinct groupings that correlate with different word senses. Finally, we show that we can provide effective acquisition techniques for novel word senses using a combination of online sources, in particular, Longman's Dictionary of Contemporary En- glish (LDOCE) (Procter, 1978), Levin's verb classifica- tion scheme (Levin, 1993), and WordNet (Miller, 1985). We have used these techniques to build a database of 10,000 English verb entries containing semantic infor- mation that we are currently porting into languages such as Arabic, Spanish, and Korean for multilingual NLP tasks such as foreign language tutoring and ma- chine translation. 322 2 Automatic Lexical Acquisition for NLP Tasks As machine-readable resources (i.e., online dictionaries, thesauri, and other knowledge sources) become read- ily available to NLP researchers, automated acquisition has become increasingly more attractive. Several re- searchers have noted that the average time needed to construct a lexical entry can be as much as 30 min- utes (see, e.g., (Neff and McCord, 1990; Copestakc et al., 1995; Walker and Amsler, 1986)). Given that we are aiming for large-scale lexicons of 20-60,000 words, automation of the acquisition process has become a ne- cessity. Previous research in automatic acquisition focuscs primarily on the use of statistical techniques, such as bilingual alignment (Church and Hanks, 1990; Kla- …

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