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<Paper uid="W03-1025">
  <Title>A Maximum Entropy Chinese Character-Based Parser</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> We present a maximum entropy Chinese character-based parser which does word-segmentation, POS tagging and parsing in a uni ed framework. The exibility of maximum entropy model allows us to integrate into the model knowledge from other sources, together with features derived automatically from training corpus. We have shown that a relatively small word-list can reduce word-segmentation error by as much as a108a11a10 a7 , and a word-segmentation F-measurea9a11a10 a3a13a12a14a7 and label F-measure a0a2a1a4a3a6a5a8a7 are obtained by the character-based parser. Our results also show that POS information is very useful for Chinese word-segmentation, but higher-level syntactic information bene ts little to wordsegmentation. null</Paragraph>
  </Section>
class="xml-element"></Paper>