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<Paper uid="P03-1008">
  <Title>Syntactic Features and Word Similarity for Supervised Metonymy Resolution</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparseness. We partially overcome this problem by integrating a thesaurus and introducing simpler grammatical features, thereby preserving precision and increasing recall.</Paragraph>
    <Paragraph position="1"> Our algorithm generalises over two levels of contextual similarity. Resulting inferences exceed the complexity of inferences undertaken in word sense disambiguation.</Paragraph>
    <Paragraph position="2"> We also compare automatic and manual methods for syntactic feature extraction.</Paragraph>
  </Section>
class="xml-element"></Paper>