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<Paper uid="P95-1039">
  <Title>Tagset P.eduction Without Information Loss</Title>
  <Section position="5" start_page="288" end_page="288" type="concl">
    <SectionTitle>
3 Conclusions
</SectionTitle>
    <Paragraph position="0"> We have shown a method for reducing a tagset used for part-of-speech tagging without losing information given by the original tagset. In a first experiment, we were able to reduce a large tagset and needed fewer parameters for the n-gram model. Additionally, tagging accuracy slightly increased, but the improvement was not significant. Further investigation will focus on criteria for cluster selection. Can we use a similarity measure of probability distributions to identify optimal clusters? How far can we reduce the tagset without losing accuracy?</Paragraph>
  </Section>
class="xml-element"></Paper>