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<Paper uid="P98-1037">
  <Title>A Concept-based Adaptive Approach to Word Sense Disambiguation</Title>
  <Section position="7" start_page="242" end_page="242" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have described a new adaptive approach to word sense disambiguation. Under this learning strategy, first contextual representation for each word sense is built from the sense definition in MRD and represented as a weighted-vector of concepts represented as word lists in a thesaurus. Then the knowledge base is applied to the text for WSD in an adaptive fashion to improve on disambiguation precision.</Paragraph>
    <Paragraph position="1"> We have demonstrated that this approach has the potential of outperforming established static approaches. This performance is achieved despite the fact no lengthy training time or a very large corpus is required. It is evident that the WSD algorithms proposed herein are simple, take up little time and space, and most importantly, require no human intervention in all phases of WSD. Sense tagging of training material, knowledge acquisition from training data, and disambiguation all are done automatically.</Paragraph>
  </Section>
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