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<Paper uid="W96-0105">
  <Title>Selective Sampling of Effective Example Sentence Sets for Word Sense Disambiguation</Title>
  <Section position="8" start_page="65" end_page="67" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper we proposed an example sampling method for example-based verb sense disambiguation. We also reported on the system's performance by way of experiments. The experiments showed that our method, which is based on the notion of training utility, has reduced the overhead for the training of the system, as well as the size of the database.</Paragraph>
    <Paragraph position="1"> As pointed out in section 1, the generalization of examples \[8, 19\] is another method for reducing the size of the database. Whether coupling these two methods would increase overall effectivity is an empirical matter requiring further exploration.</Paragraph>
    <Paragraph position="2"> Future work will include more sophisticated methods for verb sense disambiguation and methods of acquiring seeds, the acquisition of which is currently based on an existing dictionary.  sense 1 / f~ense 2 sense 1 Figure ll-a: Interpretation certainty of &amp;quot;x&amp;quot; is small because &amp;quot;x&amp;quot; lies in the intersection of distinct verb senses Figure ll-b: Interpretation certainty of &amp;quot;x&amp;quot; is small because &amp;quot;x&amp;quot; is semantically ambiguous Figure 11: Two separate scenaries where the interpretation certainty of &amp;quot;x&amp;quot; is small We will also build an experimental database for natural language processing using our example sampling method.</Paragraph>
  </Section>
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