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<Paper uid="P97-1008">
  <Title>Similarity-Based Methods For Word Sense Disambiguation</Title>
  <Section position="6" start_page="61" end_page="61" type="concl">
    <SectionTitle>
4 Conclusions
</SectionTitle>
    <Paragraph position="0"> Similarity-based language models provide an appealing approach for dealing with data sparseness. We have described and compared the performance of four such models against two classical estimation methods, the MLE method and Katz's back-off scheme, on a pseudo-word disambiguation task. We observed that the similarity-based methods perform much better on unseen word pairs, with the measure based  the base language model was MLE-ol. /~ ranged from 6 to 11 for L and from 21 to 22 for A.</Paragraph>
    <Paragraph position="1"> on the KL divergence to the average, being the best overall.</Paragraph>
    <Paragraph position="2"> We also investigated Katz's claim that one can discard singletons in the training data, resulting in a more compact language model, without significant loss of performance. Our results indicate that for similarity-based language modeling, singletons are quite important; their omission leads to significant degradation of performance. null</Paragraph>
  </Section>
class="xml-element"></Paper>