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<Paper uid="P04-1037">
  <Title>Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models</Title>
  <Section position="8" start_page="0" end_page="0" type="concl">
    <SectionTitle>
7 Conclusions and Future Work
</SectionTitle>
    <Paragraph position="0"> We have presented two novel probabilistic models for unsupervised word sense disambiguation using parallel corpora and have shown that both models outperform existing unsupervised approaches. In addition, we have shown that our second model, the Concept model, can be used to learn a sense inventory for the secondary language. An advantage of the probabilistic models is that they can easily incorporate additional information, such as context information. In future work, we plan to investigate the use of additional monolingual context. We would also like to perform additional validation of the learned secondary language sense inventory.</Paragraph>
  </Section>
class="xml-element"></Paper>