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<Paper uid="J02-2003">
  <Title>c(c) 2002 Association for Computational Linguistics Class-Based Probability Estimation Using a Semantic Hierarchy</Title>
  <Section position="9" start_page="204" end_page="205" type="concl">
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    <Paragraph position="0"> performed slightly better than G  using the smaller data set also.  Clark and Weir Class-Based Probability Estimation which is to split the count for a noun evenly among the noun's senses. Abney and Light (1999) have tried a more motivated approach, using the expectation maximization algorithm, but with little success. The approach described in Clark and Weir (1999) is shown in Clark (2001) to have some impact on the pseudo-disambiguation task, but only with certain values of the a parameter, and ultimately does not improve on the best performance.</Paragraph>
    <Paragraph position="1"> Finally, an issue that has not been much addressed in the literature (except by Li and Abe [1996]) is how the accuracy of class-based estimation techniques compare when automatically acquired classes, as opposed to the manually created classes from WordNet, are used. The pseudo-disambiguation task described here has also been used to evaluate clustering algorithms (Pereira, Tishby, and Lee, 1993; Rooth et al., 1999), but with different data, and so it is difficult to compare the results. A related issue is how the structure of WordNet affects the accuracy of the probability estimates. We have taken the structure of the hierarchy for granted, without any analysis, but it may be that an alternative design could be more conducive to probability estimation.</Paragraph>
  </Section>
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