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<Paper uid="P06-2064">
  <Title>Interpreting Semantic Relations in Noun Compounds via Verb Semantics</Title>
  <Section position="14" start_page="497" end_page="497" type="concl">
    <SectionTitle>
9 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we proposed a method for automatically interpreting noun compounds based on seed verbs indicative of each semantic relation. For a given modifier and head noun, our method extracted corpus instances of the two nouns in a range of constructional contexts, and then mapped the original verbs onto seed verbs based on lexical similarity derived from WordNet::Similarity, and Moby's Thesaurus. These instances were then fed into the TiMBL learner to build a classifier. The best-performing method was VECTOR, which is a context vector distributional similarity method. We also experimented with varying numbers of seed verbs, and found that generally the more seed verbs, the better the performance. Overall, the best-performing system achieved an accuracy of 52.6% with 84 seed verbs and the VECTOR verb-mapping method.</Paragraph>
  </Section>
class="xml-element"></Paper>