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<Paper uid="C96-1055">
  <Title>Role of Word Sense Disambiguation in Lexical Acquisition: Predicting Semantics from Syntactic Cues</Title>
  <Section position="3" start_page="322" end_page="322" type="intro">
    <SectionTitle>
2 Automatic Lexical Acquisition for
NLP Tasks
</SectionTitle>
    <Paragraph position="0"> 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 can be as much as 30 minutes (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 necessity. null Previous research in automatic acquisition focuscs 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 (Brant, 1993; Dorr, Garman, and Weinberg, 1995). Others who have taken a more knowledge-based (interlingual) approach (Lonsdale, Mitamura, and Nyberg, 1996) do not provide a means for systematically 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.</Paragraph>
    <Paragraph position="1"> 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). While the LDOCE has been used previously in automatic cxtraction tasks (Alshawi, 1989; Farwell, Guthrie, and Wilks, 1993; Boguraev and Briscoe, 1989; ,Wilks et al., 1989; Wilks et al., 1990) these tasks are primarily concerned with the extraction of other types of information including syntactic phrase structure and broad argument restrictions or with the derivation of semantic structures from definition analyses. The work of Sanfilippo and Poznanski (1992) is more closely related to our approach in that it attempts to recover a syntactic-semantic relation from machine-readable dictionaries. Itowever, they claim that the semantic classification of verbs based on standard machine-readable dictionaries (e.g., the LDOCE) is % hopeless pursuit \[since\] standard dictionaries are simply not equipped to offer this kind of information with consistency and exhaustiveness.&amp;quot; null Others have also argued that the task of simplifyin K lexical entries on the basis of broad semantic class membership is complex and, perhaps, infeasible (see, e.g., Boguraev and llriscoe (1989)). tlowever, a number of researchers (l,'ilhnore, 1968; Grimshaw, 1990; Gruber, 1965; Guthrie et al., 1991; Hearst, 1991; Jackendotr, 1983; Jackendoff, 1990; l,evin, 1993; Pinker, t989; Yarowsky, 1992) have demonstrated conclusively that there is a clear relationship between syntactic context and word senses; it is our aim to exploit this relationship for the acquisition of semantic lexicons.</Paragraph>
  </Section>
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