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<Paper uid="P98-2245">
  <Title>Bridging the Gap between Dictionary and Thesaurus</Title>
  <Section position="2" start_page="0" end_page="1487" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> It is generally accepted that applications such as word sense disambiguation (WSD), machine translation (MT) and information retrieval (IR), require a wide range of resources to supply the necessary lexical semantic information. For instance, Calzolari (1988) proposed a lexical database in Italian which has the features of both a dictionary and a thesaurus; and Klavans and Tzoukermann (1995) tried to build a fuller bilingual lexicon by enhancing machine-readable dictionaries with large corpora.</Paragraph>
    <Paragraph position="1"> Among the attempts to enrich lexical information, many have been directed to the analysis of dictionary definitions and the transformation of the implicit information to explicit knowledge bases for computational purposes (Amsler, 1981; Calzolari, 1984; Chodorow et al., 1985; Markowitz et al., 1986; Klavans et al., 1990; Vossen and Copestake, 1993). Nonetheless, dictionaries are also infamous of their non-standardised sense granularity, and the taxonomies obtained from definitions are inevitably ad hoc. It would therefore be a good idea if we can unify our lexical semantic knowledge by some existing, and widely exploited, classifications such as the system in Roget's Thesaurus (Roget, 1852), which has remained intact for years and has been used in WSD (Yarowsky, 1992).</Paragraph>
    <Paragraph position="2"> While the objective is to integrate different lexical resources, the problem is: how do we reconcile the rich but variable information in dictionary  senses with the cruder but more stable taxonomies like those in thesauri? This work is intended to fill this gap. We use WordNet as a mediator in the process. In the following, we will outline an algorithm to map word senses in a dictionary to semantic classes in some established classification scheme.</Paragraph>
  </Section>
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