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<Paper uid="W99-0503">
  <Title>merlo(c)lettres unlge ch</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> Vve zeport a number of computatmnal experiments m supervised learning whose goal Is to automatmally classify a set of verbs into lexmal semanUc classes, based on frequency dlstnbutmn approxlmatmns of grammatical features extracted from a very large annotated corpus DlstnbuUons of five syntactic features that approximate tranmUvlty alternatmns and thematic role assignments are sufficient to reduce error rate by 56% over chance We conclude that corpus data is a usable repository of verb class mformatmn, and that corpus-driven extraction of grammaUcal features Is a promising methodology for automatm lexmal acqum,Uon</Paragraph>
  </Section>
class="xml-element"></Paper>