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<Paper uid="E06-1026">
  <Title>Latent Variable Models for Semantic Orientations of Phrases</Title>
  <Section position="7" start_page="206" end_page="206" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We proposed models for phrases with semantic orientations as well as a classification method based on the models. We introduced a latent variable into the models to capture the properties of phrases. Through experiments, we showed that the proposed latent variable models work well in the classification of semantic orientations of phrases and achieved nearly 82% classification accuracy. We should also note that our method is language-independent although evaluation was on a Japanese dataset.</Paragraph>
    <Paragraph position="1"> We plan next to adopt a semi-supervised learning method in order to correctly classify phrases with infrequent words, as mentioned in Section 4.2. We would also like to extend our method to 3- or more term phrases. We can also use the obtained latent variables as features for another classifier, as Fujita et al. (2004) used latent variables of PLSI for the k-nearest neighbors method. One important and promising task would be the use of semantic orientations of words for phrase level classification.</Paragraph>
  </Section>
class="xml-element"></Paper>