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<Paper uid="P04-3028">
  <Title>Co-training for Predicting Emotions with Spoken Dialogue Data</Title>
  <Section position="6" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have shown Co-training to be a promising approach for predicting emotions with spoken dialogue data. We have given an algorithm that increased the size of the training set producing even better accuracy than the manually labeled training set, until it fell behind due to its inability to add more than 58 examples.</Paragraph>
    <Paragraph position="1"> We have shown the positive effect of selecting a good set of features optimizing precision for each learner and we have shown that the features can be identified with the Wrapper Approach.</Paragraph>
    <Paragraph position="2"> In the future, we will verify the generalization of our results to other partitions of our data. We will also try to address the limitation of noise in our Co-training System, and generalize our solution to a corresponding corpus of human-computer data (Litman and Forbes-Riley, 2004). We will also conduct experiments comparing Co-training with other semi-supervised approaches such as self-training and Active learning.</Paragraph>
  </Section>
class="xml-element"></Paper>