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<Paper uid="W01-0703">
  <Title>Learning class-to-class selectional preferences</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Previous literature on selectional preference has usually learned preferences for words in the form of classes, e.g., the object of eat is an edible entity. This paper extends previous statistical models to classes of verbs, yielding a relation between classes in a hierarchy, as opposed to a relation between a word and a class.</Paragraph>
    <Paragraph position="1"> The model is trained using subject-verb and object-verb associations extracted from Semcor, a corpus (Miller et al., 1993) tagged with WordNet word-senses (Miller et al., 1990). The syntactic relations were extracted using the Minipar parser (Lin, 1993). A peculiarity of this exercise is the use of a small sense-disambiguated corpus, in contrast to using a large corpus of ambiguous words. We think that two factors can help alleviate the scarcity of data: the fact that using disambiguated words provides purer data, and the ability to use classes of verbs in the preferences. Nevertheless, the approach can be easily extended to larger, nondisambiguated corpora.</Paragraph>
    <Paragraph position="2"> We have defined a word sense disambiguation exercise in order to evaluate the extracted preferences, using a sample of words and a sample of documents, both from Semcor.</Paragraph>
    <Paragraph position="3"> Following this short introduction, section 2 reviews selectional restriction acquisition.</Paragraph>
    <Paragraph position="4"> Section 3 explains our approach, which is formalized in sections 4 and 5. Next, section 6 shows the results on the WSD experiment. Some of the acquired preferences are analysed in section 7. Finally, some conclusions are drawn and future work is outlined.</Paragraph>
  </Section>
class="xml-element"></Paper>