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<Paper uid="W00-0721">
  <Title>Maximum entropy Markov models for information extraction and segmentation. In Proc. of ICML-</Title>
  <Section position="8" start_page="109" end_page="109" type="concl">
    <SectionTitle>
6 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have addressed the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints, While the probabilistic approach extends standard and commonly used techniques for sequential decisions, it seems that the constraint satisfaction formalisms can support complex constraints and dependencies more flexibly. Future work will concentrate on these formalisms.</Paragraph>
  </Section>
class="xml-element"></Paper>