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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="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We study the problem of identifying phrase structure. We formalize it as the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints, and develop two general approaches for it. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observations structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction formalisms. We also develop efficient algorithms under both models and study them experimentally in the context of shallow parsing. 1</Paragraph>
  </Section>
class="xml-element"></Paper>