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<Paper uid="W06-0505">
  <Title>Efficient Hierarchical Entity Classifier Using Conditional Random Fields</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper we develop an automatic classifier for a very large set of labels, the WordNet synsets. We employ Conditional Random Fields (CRFs) because of their flexibility to include a wide variety of non-independent features. Training CRFs on a big number of labels proved a problem because of the large training cost. By taking into account the hypernym/hyponym relation between synsets in WordNet, we reduced the complexity of training from O(TM2NG) to O(T (logM)2NG) with only a limited loss in accuracy.</Paragraph>
  </Section>
class="xml-element"></Paper>