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<Paper uid="W06-1406">
  <Title>Using Distributional Similarity to Identify Individual Verb Choice</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Human text is characterised by the individual lexical choices of a specific author. Significant variations exist between authors. In contrast, natural language generation systems normally produce uniform texts. In this paper we apply distributional similarity measures to help verb choice in a natural language generation system which tries to generate text similar to individual author. By using a distributional similarity (DS) measure on corpora collected from a recipe domain, we get the most likely verbs for individual authors. The accuracy of matching verb pairs produced by distributional similarityishigherthanusingthesynonymout- null puts of verbs from WordNet. Furthermore, the combination of the two methods provides the best accuracy.</Paragraph>
  </Section>
class="xml-element"></Paper>