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<Paper uid="W99-0503">
  <Title>merlo(c)lettres unlge ch</Title>
  <Section position="9" start_page="20" end_page="20" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> In thin paper, we have presented an m-depth case study, m whmh we investigate varmus machine learnmg techmques to automatically classify a set of verbs, based on dlstnbutmnal features extracted from a very large corpus Results show that a small number of hngmstlcally motivated grammatical features are sufficmnt to reduce the error rate by mote than 50% over chance, acluevmg a 70% acctuacy rate m a three-way classfficatmn task Tins leads us to conclude that corpus data is a usable repository of verb class mformatmn On one hand ~e observe that semantlc propemes of verb classes (such as causatlvlty, or ammacy of subject) may be usefully approximated through countable syntactic features Even with some noise, lexmal propertms are reflected m the corpus robustly enough to positively contribute m classlficatmn On the other hand, however, we remark that deep hngumtm analysis cannot be ehmmated--m our approach, it is embedded m the selection of the features to count We also think that using hngumtlcally motivated features makes the approach very effective and easdy scalable we report a 56% reductmn m error rate, w~th only five features that are relatwely straightforward to count</Paragraph>
  </Section>
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