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<Paper uid="W96-0113">
  <Title>A Re-estimation Method for Stochastic Language Modeling from Ambiguous Observations</Title>
  <Section position="9" start_page="165" end_page="165" type="concl">
    <SectionTitle>
7 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have proposed an estimation method from ambiguous observations and a credit factor.</Paragraph>
    <Paragraph position="1"> This estimation method can use untagged, unsegmented language corpora as training data and build not only the N-gram model, but also the HMM. A credit factor can improve the reliability of the model estimated from an untagged corpus.</Paragraph>
    <Paragraph position="2"> This method can be further improved and integrated with other language models. In particular, it is important to formulate a dynamic method to assign the credit factor based on small sets of tagged data for development.</Paragraph>
  </Section>
class="xml-element"></Paper>