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<Paper uid="W06-1649">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics Partially Supervised Sense Disambiguation by Learning Sense Number from Tagged and Untagged Corpora</Title>
  <Section position="9" start_page="420" end_page="421" type="relat">
    <SectionTitle>
4 Related Work
</SectionTitle>
    <Paragraph position="0"> The work closest to ours is partially supervised classi cation or building classi ers using positive examples and unlabeled examples, which has been studied in machine learning community (Denis et al., 2002; Liu et al., 2003; Manevitz and Yousef, 2001; Yu et al., 2002). However, they cannot  group negative examples into meaningful clusters. In contrast, our algorithm can nd the occurrence of negative examples and further group these negative examples into a natural number of clusters. Semi-supervised clustering (Wagstaff et al., 2001) may be used to perform classi cation by the use of labeled and unlabeled examples, but it encounters the same problem of partially supervised classi cation that model order cannot be automatically estimated.</Paragraph>
    <Paragraph position="1"> Levine and Domany (2001) and Lange et al.</Paragraph>
    <Paragraph position="2"> (2002) proposed cluster validation based criteria for cluster number estimation. However, they showed the application of the cluster validation method only for unsupervised learning. Our work can be considered as an extension of their methods in the setting of partially supervised learning.</Paragraph>
    <Paragraph position="3"> In natural language processing community, the work that is closely related to ours is word sense discrimination which can induce senses by grouping occurrences of a word into clusters (Sch&amp;quot;utze, 1998). If it is considered as unsupervised methods to solve sense disambiguation problem, then our method employs partially supervised learning technique to deal with sense disambiguation problem by use of tagged and untagged texts.</Paragraph>
  </Section>
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