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<Paper uid="W06-2007">
  <Title>Word Sense Disambiguation Using Automatically Translated Sense Examples</Title>
  <Section position="7" start_page="50" end_page="51" type="concl">
    <SectionTitle>
6 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> We automatically acquired English sense examples for WSD using large Chinese corpora and MT software. We compared our sense examples with those reported in previous work (Wang and Car- null tems for nouns (sorted by recall(%)). Our system given in bold.</Paragraph>
    <Paragraph position="1"> roll, 2005), by training a ML classifier on them and then testing the classifiers on both coarse-grained and fine-grained English gold standard datasets. On both datasets, our MT-based sense examples outperformed dictionary-based ones. In addition, evaluations show our unsupervised WSD system is competitive to the state-of-the-art supervised systems on binary disambiguation, and unsupervised systems on fine-grained disambiguation. null In the future, we would like to combine our approach with other systems based on automatic acquisition of sense examples that can provide local context (Agirre and Martinez, 2004b). The goal would be to construct a collection of examples automatically obtained from different sources and to apply ML algorithms on them. Each example would have a different weight depending on the acquisition method used.</Paragraph>
    <Paragraph position="2"> Regarding the influence of sense distribution in the training data, we will explore the potential of using a weighting scheme on the &amp;quot;relative threshold&amp;quot; algorithm. Also, we would like to analyse if automatically obtained information on sense distribution (McCarthy et al., 2004) can improve WSD performance. We may also try other MT systems and possibly see if our WSD can in turn help MT, which can be viewed as a bootstrapping learning process. Another interesting direction is automatically selecting the most informative sense examples as training data for ML classifiers.</Paragraph>
  </Section>
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