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<Paper uid="P04-1048">
  <Title>Inducing Frame Semantic Verb Classes from WordNet and LDOCE</Title>
  <Section position="3" start_page="0" end_page="0" type="intro">
    <SectionTitle>
2 Previous Work
</SectionTitle>
    <Paragraph position="0"> The EAGLES (1998) report on semantic encoding differentiates between two approaches to the development of semantic verb classes: those based on syntactic behavior and those based on semantic criteria.</Paragraph>
    <Paragraph position="1"> Levin (1993) groups verbs based on an analysis of their syntactic properties, especially their ability to be expressed in diathesis alternations; her approach reflects the assumption that the syntactic behavior of a verb is determined in large part by its meaning. Verb classes at the bottom of Levin's shallow network group together (quasi-) synonyms, hierarchically related verbs, and antonyms, alongside verbs with looser semantic relationships.</Paragraph>
    <Paragraph position="2"> The verb categories based on Pantel and Lin (2002) and Lin and Pantel (2001) are induced automatically from a large corpus, using an unsupervised clustering algorithm, based on syntactic dependency features. The resulting clusters contain synonyms, hierarchically related verbs, and antonyms, as well as verbs more loosely related from the perspective of paraphrase.</Paragraph>
    <Paragraph position="3"> The handcrafted WordNet (Fellbaum, 1998a) uses the hyperonymy/hyponymy relationship to structure the English verb lexicon into a semantic network. Each collection of a top-level node supplemented by its descendants may be seen as a semantic verb class.</Paragraph>
    <Paragraph position="4"> In all fairness, resolution of the paraphrase problem is not the explicit goal of most efforts to build semantic verb classes. However, they can process some paraphrases through lexical synonymy, hierarchically related terms, and antonymy.</Paragraph>
  </Section>
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