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<Paper uid="P99-1014">
  <Title>Detlef Prescher</Title>
  <Section position="7" start_page="109" end_page="110" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have proposed a procedure which maps observations of subcategorization frames with their complement fillers to structured lexical entries. We believe the method is scientifically interesting, practically useful, and flexible because: null 1. The algorithms and implementation are efficient enough to map a corpus of a hundred million words to a lexicon.</Paragraph>
    <Paragraph position="1">  2. The model and induction algorithm have foundations in the theory of parameterized families of probability distributions and statistical estimation. As exemplified in the paper, learning, disambiguation, and evaluation can be given simple, motivated formulations.</Paragraph>
    <Paragraph position="2"> 3. The derived lexical representations are linguistically interpretable. This suggests the possibility of large-scale modeling and observational experiments bearing on questions arising in linguistic theories of the lexicon. null 4. Because a simple probabilistic model is used, the induced lexical entries could be incorporated in lexicalized syntax-based probabilistic language models, in particular in head-lexicalized models. This provides for potential application in many areas.</Paragraph>
    <Paragraph position="3"> 5. The method is applicable to any natural language where text samples of sufficient size, computational morphology, and a robust parser capable of extracting subcategorization frames with their fillers are available. null</Paragraph>
  </Section>
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