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<Paper uid="P05-1006">
  <Title>The Role of Semantic Roles in Disambiguating Verb Senses</Title>
  <Section position="7" start_page="48" end_page="48" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have shown that disambiguation of verb senses can be improved by leveraging information about predicate arguments and their semantic classes. Our system performs at the best published accuracy on the English verbs of Senseval-2 even though our heuristics for extracting syntactic features fail to identify all and only the arguments of a verb. We show that associating WordNet semantic classes with nouns is beneficial even without explicit disambiguation of the noun senses because, given enough data, maximum entropy models are able to assign high weights to the correct hypernyms of the correct noun sense if they represent defining selectional restrictions. Knowledge of gold-standard predicate-argument information from PropBank improves WSD on both coarse-grained senses (Prop-Bank framesets) and fine-grained WordNet senses.</Paragraph>
    <Paragraph position="1"> Furthermore, partitioning instances according to their gold-standard frameset tags, which are based on differences in subcategorization frames, also improves the system's accuracy on fine-grained Word-Net sense-tagging. Our experiments suggest that sense disambiguation for verbs can be improved through more accurate extraction of features representing information such as that contained in the framesets and predicate argument structures annotated in PropBank.</Paragraph>
  </Section>
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