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<Paper uid="P01-1032">
  <Title>Mapping Lexical Entries in a Verbs Database to WordNet Senses</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes automatic techniques for mapping 9611 entries in a database of English verbs to Word-Net senses. The verbs were initially grouped into 491 classes based on syntactic features. Mapping these verbs into WordNet senses provides a resource that supports disambiguation in multilingual applications such as machine translation and cross-language information retrieval. Our techniques make use of (1) a training set of 1791 disambiguated entries, representing  1442 verb entries from 167 classes; (2) word sense probabilities, from frequency counts in a tagged corpus; (3) semantic similarity of WordNet senses for verbs within the same class; (4) probabilistic correlations between  WordNet data and attributes of the verb classes. The best results achieved 72% precision and 58% recall, versus a lower bound of 62% precision and 38% recall for assigning the most frequently occurring WordNet sense, and an upper bound of 87% precision and 75% recall for human judgment.</Paragraph>
  </Section>
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