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<Paper uid="W01-0502">
  <Title>A Sequential Model for Multi-Class Classificationa0</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> A wide range and a large number of classification tasks will have to be used in order to perform any high level natural language inference such as speech recognition, machine translation or question answering. Although in each instantiation the real conflict could be only to choose among a small set of candidates, the original set of candidates could be very large; deriving the small set of candidates that are relevant to the task at hand may not be immediate. null This paper addressed this problem by developing a general paradigm for multi-class classification that sequentially restricts the set of candidate classes to a small set, in a way that is driven by the data observed. We have described the method and provided some justifications for its advantages, especially in NLP-like domains. Preliminary experiments also show promise.</Paragraph>
    <Paragraph position="1"> Several issues are still missing from this work.</Paragraph>
    <Paragraph position="2"> In our experimental study the decomposition of the feature space was done manually; it would be nice to develop methods to do this automatically. Better understanding of methods for thresholding the probability distributions that the classifiers output, as well as principled ways to order them are also among the future directions of this research.</Paragraph>
  </Section>
class="xml-element"></Paper>