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<Paper uid="P97-1059">
  <Title>Finite State Transducers Approximating Hidden Markov Models</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors.</Paragraph>
    <Paragraph position="1"> The speed of tagging is also improved. The described methods have been implemented and successfully tested on six languages.</Paragraph>
  </Section>
class="xml-element"></Paper>