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<Paper uid="P06-1101">
  <Title>Semantic Taxonomy Induction from Heterogenous Evidence</Title>
  <Section position="7" start_page="807" end_page="807" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have presented an algorithm for inducing semantic taxonomies which attempts to globally optimize the entire structure of the taxonomy.</Paragraph>
    <Paragraph position="1"> Our probabilistic architecture also includes a new model for learning coordinate terms based on (m,n)-cousin classification. The model's ability to integrate heterogeneous evidence from different classifiers offers a solution to the key problem of choosing the correct word sense to which to attach a new hypernym.</Paragraph>
  </Section>
class="xml-element"></Paper>