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<Paper uid="P93-1032">
  <Title>AUTOMATIC ACQUISITION OF A LARGE SUBCATEGORIZATION DICTIONARY FROM CORPORA</Title>
  <Section position="9" start_page="241" end_page="241" type="concl">
    <SectionTitle>
CONCLUSION
</SectionTitle>
    <Paragraph position="0"> After establishing that it is desirable to be able to automatically induce the subcategorization frames of verbs, this paper examined a new technique for doing this. The paper showed that the technique of trying to learn from easily analyzable pieces of data is not extendable to all subcategorization frames, and, at any rate, the sparseness of appropriate cues in unrestricted texts suggests that a better strategy is to try and extract as much (noisy) information as possible from as much of the data as possible, and then to use statistical techniques to filter the results. Initial experiments suggest that this technique works at least as well as previously tried techniques, and yields a method that can learn all the possible subcategorization frames of verbs.</Paragraph>
  </Section>
class="xml-element"></Paper>