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<Paper uid="P94-1025">
  <Title>PART-OF-SPEECH TAGGING USING A VARIABLE MEMORY MARKOV MODEL Hinrich Schiitze Center for the Study of Language and Information</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We present a new approach to disambiguating syntactically ambiguous words in context, based on Variable Memory Markov (VMM) models. In contrast to fixed-length Markov models, which predict based on fixed-length histories, variable memory Markov models dynamically adapt their history length based on the training data, and hence may use fewer parameters. In a test of a VMM based tagger on the Brown corpus, 95.81% of tokens are correctly classified.</Paragraph>
  </Section>
class="xml-element"></Paper>