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<Paper uid="P97-1007">
  <Title>Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation *</Title>
  <Section position="8" start_page="53" end_page="53" type="concl">
    <SectionTitle>
6 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> The results show that computer aided construction of taxonomies using lexical resources is not limited to highly-structured dictionaries as LDOCE, but has been succesfully achieved with two very different dictionaries. All the heuristics used are unsupervised, in the sense that they do not need hand-codding of any kind, and the proposed method can be adapted to any dictionary with minimal parameter setting.</Paragraph>
    <Paragraph position="1"> Nevertheless, quality and size of the lexical knowledge resources are important. As the results for LPPL show, small dictionaries with short definitions can not profit from raw corpus techniques (heuristics 5, 6), and consequently the improvement of precision over the random baseline or first-sense heuristic is lower than in DGILE.</Paragraph>
    <Paragraph position="2"> We have also shown that such a simple technique as just summing is a useful way to combine knowledge from several unsupervised WSD methods, allowing to raise the performance of each one in isolation (coverage and/or precision). Furthermore, even those heuristics with apparently poor results provide knowledge to the final result not provided by the rest of heuristics. Thus, adding new heuristics with different methodologies and different knowledge (e.g.</Paragraph>
    <Paragraph position="3"> from corpora) as they become available will certainly improve the results.</Paragraph>
    <Paragraph position="4"> Needless to say, several improvements can be done both in individual heuristic and also in the method to combine them. For instance, the cooccurfence heuristics have been applied quite indiscriminately, even in low frequency conditions. Significance tests or association coefficients could be used in order to discard low confidence decisions. Also, instead of just summing, more clever combinations can be tried, such as training classifiers which use the heuristics as predictor variables.</Paragraph>
    <Paragraph position="5"> Although we used these techniques for genus disambiguation we expect similar results (or even better taken the &amp;quot;one sense per discourse&amp;quot; property and lexical knowledge acquired from corpora) for the WSD problem.</Paragraph>
  </Section>
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