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<Paper uid="P05-1017">
  <Title>Extracting Semantic Orientations of Words using Spin Model</Title>
  <Section position="6" start_page="138" end_page="139" type="concl">
    <SectionTitle>
6 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We proposed a method for extracting semantic orientations of words. In the proposed method, we regarded semantic orientations as spins of electrons, and used the mean field approximation to compute the approximate probability function of the system instead of the intractable actual probability function.</Paragraph>
    <Paragraph position="1"> We succeeded in extracting semantic orientations with high accuracy, even when only a small number of seed words are available.</Paragraph>
    <Paragraph position="2"> There are a number of directions for future work.</Paragraph>
    <Paragraph position="3"> One is the incorporation of syntactic information.</Paragraph>
    <Paragraph position="4"> Since the importance of each word consisting a gloss depends on its syntactic role. syntactic information in glosses should be useful for classification.</Paragraph>
    <Paragraph position="5"> Another is active learning. To decrease the amount of manual tagging for seed words, an active learning scheme is desired, in which a small number of good seed words are automatically selected.</Paragraph>
    <Paragraph position="6"> Although our model can easily extended to a  multi-state model, the effectiveness of using such a multi-state model has not been shown yet.</Paragraph>
    <Paragraph position="7"> Our model uses only the tendency of having the same orientation. Therefore we can extract semantic orientations of new words that are not listed in a dictionary. The validation of such extension will widen the possibility of application of our method.</Paragraph>
    <Paragraph position="8"> Larger corpora such as web data will improve performance. The combination of our method and the method by Turney and Littman (2003) is promising.</Paragraph>
    <Paragraph position="9"> Finally, we believe that the proposed model is applicable to other tasks in computational linguistics.</Paragraph>
  </Section>
class="xml-element"></Paper>