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<Paper uid="W97-0201">
  <Title>Getting Serious about Word Sense Disambiguation</Title>
  <Section position="7" start_page="5" end_page="5" type="concl">
    <SectionTitle>
5 Can We Do Better?
</SectionTitle>
    <Paragraph position="0"> My estimate of the amount of human annotation effort needed can be considered as an upper bound on the manual effort needed to construct the necessary sense-tagged corpus to achieve wide coverage WSD.</Paragraph>
    <Paragraph position="1"> It may turn out that we can achieve our goal with much less annotation effort.</Paragraph>
    <Paragraph position="2"> Recent work on intelligent example selection techniques suggest that the quality of the examples used for supervised learning can have a large impact on the classification accuracy of the induced classitier. For example, in (Engelson and Dagan, 1996),  and the bottom 20%, ..., bottom 1% of word occurrences.  ..., top 99%, and the bottom 20%, ..., bottom 1% of word occurrences. committee-based sample selection is applied to part-of-speech tagging to select for annotation only those examples that are the most informative, and this avoids redundantly annotating examples. Similarly, in (Lewis and Catlett, 1994), uncertainty sampling of training examples achieved better accuracy than random sampling of training examples for a text categorization application.</Paragraph>
    <Paragraph position="3"> Intelligent example selection for supervised learning is an important issue of machine learning in its own right. I believe it is of particular importance to investigate this issue in the context of word sense disambiguation, as the payoff is high, given that a large sense tagged corpus is currently not available and remains one of the most critical bottlenecks in achieving wide coverage, high accuracy WSD.</Paragraph>
  </Section>
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