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<Paper uid="W99-0622">
  <Title>Guiding a Well-Founded Parser with Corpus Statistics</Title>
  <Section position="9" start_page="183" end_page="184" type="concl">
    <SectionTitle>
8 Conclusion
</SectionTitle>
    <Paragraph position="0"> This work suggests that despite their low frequency, vt-ary lexical statistics can be combined with an ontology, such as WordNet, to be used to aid parsing and word sense disambiguation.</Paragraph>
    <Paragraph position="1"> More interestingly, results from our small corpus indicate that WordNet (or some ontology) is necessary for n-ary statistics to be useful. In addition, these results can be obtained within the framework of a well-founded grammar and lexicon. All of this together yields a broad-coverage parser that lends itself to applications requiring natural language understanding. In the future, we hope to improve our model and expand our corpus, and thus to improve our parsing accuracy further.</Paragraph>
    <Section position="1" start_page="183" end_page="184" type="sub_section">
      <SectionTitle>
8.1 Acknowledgments
</SectionTitle>
      <Paragraph position="0"> The grammar and parser we are using are generously supplied by The Boeing Company. Many thanks to Phil Harrison at Boeing for answering  all our questions about SAPIR, and for help- ambiguating word senses in a large corpus. ing to make it available in the first place. This Computers and the Humanities, 26:415-439, material is based upon work supported by NSF December.</Paragraph>
      <Paragraph position="1"> grantsIRI-9503312, IRI-9623665, andIRI-9711009. Ralph Grishman, Catherine Macleod, and Adam Meyers. 1994. COMLEX syntax:</Paragraph>
    </Section>
  </Section>
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