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<Paper uid="W02-1111">
  <Title>Fine-Grained Proper Noun Ontologies for Question Answering</Title>
  <Section position="5" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> In this paper we have motivated the use of a proper noun ontology for question answering. We described a method for inducing pieces of this ontology, and then showed preliminary methods can be useful. Prior work on proper nouns has focused on classifying them into very coarse categories (e.g. PERSON, LOCATION). As this paper has shown, these coarse classifications can be refined fortuitously, especially for the PERSON type.</Paragraph>
    <Paragraph position="1"> This paper demonstrates that inducing a general ontology improves question answering performance. Previous work examined ontology induction for a specialized domain. It is somewhat surprising that an ontology built from unrestricted text can lead to improvement on unmatched questions.</Paragraph>
    <Paragraph position="2"> The experiments we performed demonstrated that though the precision of the ontology is high, the crucial problem is increasing coverage. Tackling this problem is an important area of future work. Finally, this work opens up a potential new avenue for work on inducing proper noun ontologies. There are doubtlessly many more ways to extract descriptions and to improve coverage.</Paragraph>
  </Section>
class="xml-element"></Paper>