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<Paper uid="W93-0102">
  <Title>Product Formation Vector Network Bayesian Vector Network</Title>
  <Section position="6" start_page="18" end_page="18" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> The capacity to determine the intended sense of an ambiguous word is an important component of any general system for language understanding. We believe that, in order to accomplish this task, we need contextual representations of word senses containing both topical and local context. Initial experiments focused on methods that are able to extract topical context. These methods are effective, but topical context alone is not sufficient for sense resolution tasks. The human subject experiment shows that even people are not very good at resolving senses when given only topical context. Currently we are testing methods for learning local context for word senses. Preliminary results show that the addition of template matching on local context improves performance.</Paragraph>
  </Section>
class="xml-element"></Paper>