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<Paper uid="H93-1054">
  <Title>SEMANTIC CLASSES AND SYNTACTIC AMBIGUITY</Title>
  <Section position="7" start_page="282" end_page="282" type="concl">
    <SectionTitle>
5. Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper, we have used a knowledge-based conceptual taxonomy, together with corpus-based lexical statistics, to provide new formalizations of selectional preference and semantic similarity. Although a complete characterization of these and other semantic notions may ultimately turn out to require a full-fledged theory of meaning, lexical-conceptual representation, and inference, we hope to have shown that a great deal can be accomplished using a simple semantic representation combined with appropriate information-theoretic ideas. Conversely, we also hope to have shown the utility of knowledge-based semantic classes in arriving at a statistical characterization of linguistic phenomena, as compared to purely distributional methods. A detailed comparison of knowledge-based and distributionally-derived word classes is needed in order to assess the advantages and disadvantages of each approach.</Paragraph>
    <Paragraph position="1"> &amp;quot;Every way ambiguous&amp;quot; constructions form a natural class of practical problems to investigate using class-based statistical techniques. The present results are promising, and we are exploring improvements to the particular algorithms and results illustrated here. In future work we hope to investigate other ambiguous constructions, and to explore the implications of selectional preference for word-sense disambiguation.</Paragraph>
  </Section>
class="xml-element"></Paper>