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<Paper uid="W06-0122">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics On Using Ensemble Methods for Chinese Named Entity Recognition</Title>
  <Section position="6" start_page="144" end_page="144" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this paper, we use ME and CRF models to train a Chinese named entity tagger. Like previous researchers, we found that CRF models out-perform ME models. We also apply two ensemble methods, namely, majority vote and memory-based approaches, to the closed NER shared task. Our results show that integrating individual classifiers as the majority vote approach does not outperform the individual classifiers. Furthermore, a memory-based combination only seems to work when we restrict the memory-based classifier to handling person names.</Paragraph>
  </Section>
class="xml-element"></Paper>