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<Paper uid="A00-3007">
  <Title>Word Sense Disambiguation for Cross-Language Information Retrieval</Title>
  <Section position="8" start_page="38" end_page="38" type="concl">
    <SectionTitle>
6 Concluding Remarks
</SectionTitle>
    <Paragraph position="0"> The ambiguity of words may negatively impact the retrieval performance of a concept-based information retrieval system like CINDOR. We have developed a WSD algorithm that uses all the words in a WordNet symet as evidence of a given sense and builds an association matrix to learn the co-occurrence between words and senses. An evaluation of our algorithm against human judgements of a small sample of nouns demonstrated no significant difference between our automatic ranking of senses and the human judgements. There was, however, a significant difference between human judgement and rankings produced with no disambiguation where all senses were tied.</Paragraph>
    <Paragraph position="1"> These early results are such as to encourage us to continue our research in this area. In our future work we must tackle issues associated with the fine granularity of some WordNet sense distinctions, synsets which are proper subsets of other synsets and are therefore impossible to distinguish, and also extend our evaluation to multiple languages and to other parts of speech.</Paragraph>
    <Paragraph position="2"> The next step in our work will be to evaluate our WSD algorithm against the manually sense-tagged SemCor Corpus for validation, and then integrate our WSD algorithm into CINDOR's processing and evaluate directly the impact on retrieval performance. We hope to verify that word sense disambiguation leads to improved precision in cross-language retrieval.</Paragraph>
  </Section>
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