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<Paper uid="P06-2028">
  <Title>Using Lexical Dependency and Ontological Knowledge to Improve a Detailed Syntactic and Semantic Tagger of English</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper presents a detailed study of the integration of knowledge from both dependency parses and hierarchical word ontologies into a maximum-entropy-based tagging model that simultaneously labels words with both syntax and semantics.</Paragraph>
    <Paragraph position="1"> Our findings show that information from both these sources can lead to strong improvements in overall system accuracy: dependency knowledge improved performanceoverallclassesofword,andknowl- null edge of the position of a word in an ontological hierarchy increased accuracy for words not seen in the training data. The resulting tagger offers the highest reported tagging accuracy on this tagset to date.</Paragraph>
  </Section>
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