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<Paper uid="W04-0829">
  <Title>WSD Based on Mutual Information and Syntactic Patterns</Title>
  <Section position="5" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusions and future work
</SectionTitle>
    <Paragraph position="0"> The official results clearly show that the dependency of the system on the first sense heuristic is very strong. We should have been more confident in our heuristics so that maybe a linear combination giving more weight to them in opposition to the first sense baseline would have produced better results.</Paragraph>
    <Paragraph position="1"> The supervised extension of the algorithm, in which the syntactic patterns are learnt from the training examples as well as from the synset's glosses doesn't offer any improvement at all. The simple explanation is that the increase in the number of rules from the unsupervised heuristic to the supervised extension is only 17% so no changes are noticeable at the answer level.</Paragraph>
    <Paragraph position="2"> The results for the two heuristics are very encouraging. There are several points that deserve further investigation. It should be relatively easy to detect Yarowsky's collocations from a parse tree and that is likely to offer even better results in terms of precision, although the potential for increased coverage is unclear. As far as the other heuristic is concerned, it seems worthwhile to spend some time determining syntactic patterns more accurately. A good point to start could be statistical language modeling over large corpora, now that we have adapted the existing resources and parsing massive text collections is relatively easy. Of course, a WSD system aimed for final applications should also take advantage of other knowledge sources researched in previous work.</Paragraph>
  </Section>
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