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<Paper uid="W03-1018">
  <Title>Evaluation and Extension of Maximum Entropy Models with Inequality Constraints</Title>
  <Section position="9" start_page="10" end_page="10" type="concl">
    <SectionTitle>
7 Conclusion and Future Work
</SectionTitle>
    <Paragraph position="0"> We have shown that the inequality ME models outperform the cut-off method and the Gaussian MAP estimation, using the two text categorization datasets. Besides, the inequality ME models achieved high accuracies with a small number of features due to the sparseness of the solution. However, it is an open question how the inequality ME model differs from other sophisticated methods of feature selection based on other criteria.</Paragraph>
    <Paragraph position="1"> Future work will investigate the details of the inequality model including the effect of the penalty constants of the 2-norm extension. Evaluations on other NLP tasks are also planned. In addition, we need to analyze the inequality ME model further to  clarify the reasons for its success.</Paragraph>
    <Paragraph position="2"> Acknowledgments We would like to thank Yusuke Miyao, Yoshimasa Tsuruoka, and the anonymous reviewers for many helpful comments.</Paragraph>
  </Section>
class="xml-element"></Paper>