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<Paper uid="W06-0120">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics On Closed Task of Chinese Word Segmentation: An Improved CRF Model Coupled with Character Clustering and Automatically Generated Template Matching</Title>
  <Section position="2" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> This paper addresses two major problems in closed task of Chinese word segmentation (CWS): tagging sentences interspersed with non-Chinese words, and long named entity (NE) identification. To resolve the former, we apply K-means clustering to identify non-Chinese characters, and then adopt a two-tagger architecture: one for Chinese text and the other for non-Chinese text. For the latter problem, we apply postprocessing to our CWS output using automatically generated templates. The experiment results show that, when non-Chinese characters are sparse in the training corpus, our two-tagger method significantly improves the segmentation of sentences containing non-Chinese words. Identification of long NEs and long words is also enhanced by template-based postprocessing. In the closed task of SIGHAN 2006 CWS, our system achieved F-scores of 0.957, 0.972, and 0.955 on the CKIP, CTU, and MSR corpora respectively.</Paragraph>
  </Section>
class="xml-element"></Paper>