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<Paper uid="W04-0859">
  <Title>The University of Alicante systems at SENSEVAL-3/</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
4 Conclusions
</SectionTitle>
    <Paragraph position="0"> The supervised systems for the English and Spanish lexical sample tasks are very competitive. Although the processing of the train and test data was different for each task, both systems rely on retraining, a bootstrapping method, that uses a maximum entropy-based WSD module.</Paragraph>
    <Paragraph position="1"> The results for the English task prove that re-training is capable of maintaining a high level of precision. Nevertheless, for the Spanish task, although the scores achieved were excellent, the system must be redesigned in order to improve the classifiers. null The re-training method is a proposal that we are trying to incorporate into text retrieval and question answering systems that could take advantage of sense disambiguation of a subset of words.</Paragraph>
    <Paragraph position="2"> The unsupervised systems presented here does not appear to be sufficient for a stand-alone WSD solution. Wether these methods can be combined with other supervised methods to improve their results requires further investigation.</Paragraph>
  </Section>
class="xml-element"></Paper>