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<Paper uid="W03-1026">
  <Title>HowtogetaChineseName(Entity): Segmentation and Combination Issues</Title>
  <Section position="8" start_page="0" end_page="0" type="relat">
    <SectionTitle>
5 Related Work
</SectionTitle>
    <Paragraph position="0"> Sun et al. (2002) proposes to use a class-based model for Chinese NE recognition. Specifically, it uses a character-based trigram model for the class person, a word-based model for the class location, and a more complicated model for the class organization. This decision is consistent with our observation that the character-based model performs better than the word-based model for classes such as person, but is worse for classes such as organization.</Paragraph>
    <Paragraph position="1"> Sekine and Eriguchi (2000) provides an overview of Japanese NE recognition. It presents the results of 15 systems that participated in an evaluation project for Information Retrieval and Information Extraction (IREX, 1999). Utsuro et al. (2002) studies combining outputs of multiple Japanese NE systems by stacking. A second stage classifier - in this case, a decision list - is trained to combine the outputs from first stage classifiers. This is similar  in spirit to our application of the RRM classifier for combining classifier outputs.</Paragraph>
    <Paragraph position="2"> Classifier combination has been shown to be effective in improving the performance of NLP applications, and have been investigated by Brill and Wu (1998) and van Halteren et al. (2001) for part-of-speech tagging, Tjong Kim Sang et al. (2000) for base noun phrase chunking, and Florian et al. (2003a) for word sense disambiguation. Among the investigated techniques were voting, probability interpolation, and classifier stacking. We also applied the classifier combination technique discussed in this paper to English and German (Florian et al., 2003b).</Paragraph>
  </Section>
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