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<Paper uid="P98-2214">
  <Title>General-to-Specific Model Selection for Subcategorization Preference*</Title>
  <Section position="7" start_page="1320" end_page="1320" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper proposed a novel method for learning probability models of subcategorization preference of verbs. Especially, we proposed a new model selection algorithm which starts from the most general model and gradually examines more specific models. In the experimental evaluation, it is shown that both of the case dependencies and specific sense restriction selected by the proposed method contribute to improving the performance in subcategorization preference resolution. As for future works, it is important to evaluate the performance of the learned subcategorization preference model in the real parsing task.</Paragraph>
  </Section>
class="xml-element"></Paper>