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<Paper uid="W97-0319">
  <Title>Probabilistic Coreference in Information</Title>
  <Section position="10" start_page="171" end_page="171" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> Certain applications require that the output of an information extraction system be probabilistic, so that a downstream system can reliably \]use the output with possibly contradictory information from other sources. In this paper we considered the problem of assigning a probability distribution to alternative sets of coreference relationships among entity descriptions. We presented the encouraging results of initial experiments with several approaches to estimating such distributions in an application using SRI's FASTUS information extraction system. We would expect further gains from encoding additional training data and modeling more informative characteristics of context.</Paragraph>
  </Section>
class="xml-element"></Paper>