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<Paper uid="W02-1006">
  <Title>An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation</Title>
  <Section position="3" start_page="0" end_page="0" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> There is a large body of prior research on WSD. Due to space constraints, we will only highlight prior research efforts that have investigated (1) contribution of various knowledge sources, or (2) relative performance of different learning algorithms.</Paragraph>
    <Paragraph position="1"> Early research efforts on comparing different Association for Computational Linguistics.</Paragraph>
    <Paragraph position="2"> Language Processing (EMNLP), Philadelphia, July 2002, pp. 41-48. Proceedings of the Conference on Empirical Methods in Natural learning algorithms (Mooney, 1996; Pedersen and Bruce, 1997) tend to base their comparison on only one word or at most a dozen words. Ng (1997) compared two learning algorithms, k-nearest neighbor and Naive Bayes, on the DSO corpus (191 words).</Paragraph>
    <Paragraph position="3"> Escudero et al. (2000) evaluated k-nearest neighbor, Naive Bayes, Winnow-based, and LazyBoosting algorithms on the DSO corpus. The recent work of Pedersen (2001a) and Zavrel et al. (2000) evaluated a variety of learning algorithms on the SENSEVAL-1 data set. However, all of these research efforts concentrate only on evaluating different learning algorithms, without systematically considering their interaction with knowledge sources.</Paragraph>
    <Paragraph position="4"> Ng and Lee (1996) reported the relative contribution of different knowledge sources, but on only one word &amp;quot;interest&amp;quot;. Stevenson and Wilks (2001) investigated the interaction of knowledge sources, such as part-of-speech, dictionary definition, subject codes, etc. on WSD. However, they do not evaluate their method on a common benchmark data set, and there is no exploration on the interaction of knowledge sources with different learning algorithms.</Paragraph>
    <Paragraph position="5"> Participating systems at SENSEVAL-1 and SENSEVAL-2 tend to report accuracy using a particular set of knowledge sources and some particular learning algorithm, without investigating the effect of varying knowledge sources and learning algorithms. In SENSEVAL-2, the various Duluth systems (Pedersen, 2001b) attempted to investigate whether features or learning algorithms are more important. However, relative contribution of knowledge sources was not reported and only two main types of algorithms (Naive Bayes and decision tree) were tested.</Paragraph>
    <Paragraph position="6"> In contrast, in this paper, we systematically vary both knowledge sources and learning algorithms, and investigate the interaction between them. We also base our evaluation on both SENSEVAL-2 and SENSEVAL-1 official test data sets, and compare with the official scores of participating systems.</Paragraph>
  </Section>
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