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<Paper uid="W02-1815">
  <Title>CombiningClassifiersforChineseWordSegmentation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. First, a maximum entropytaggeristrainedonmanuallyannotated datatoautomaticallylabelsthecharacterswith tagsthatindicatethepositionofcharacterwithin a word. An error-driven transformation-based tagger is then trained to clean up the tagging inconsistencies of the first tagger. The tagged output is then converted into segmented text.</Paragraph>
    <Paragraph position="1"> Thepreliminaryresultsshowthatthisapproach is competitive comparedwith othersupervised machine-learning segmenters reported in previousstudies.</Paragraph>
  </Section>
class="xml-element"></Paper>