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<Paper uid="W04-3248">
  <Title>A New Approach for English-Chinese Named Entity Alignment</Title>
  <Section position="7" start_page="11" end_page="11" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> Traditional word alignment approaches cannot come up with satisfactory results for Named Entity alignment. In this paper, we propose a novel approach using a maximum entropy model for NE alignment. To ease the training of the MaxEnt model, bootstrapping is used to help supervised learning. Unlike previous work reported in the literature, our work conducts bilingual Named Entity alignment without word segmentation for Chinese, and its performance is much better than with word segmentation. When compared with IBM and HMM alignment models, experimental results show that our approach outperforms IBM Model 4 and HMM significantly.</Paragraph>
    <Paragraph position="1"> Due to the inconsistency of NE translation, some NE pairs can not be aligned correctly. We may need some manually-generated rules to fix this. We also notice that NER performance over the source language can be improved using bilingual knowledge. These problems will be investigated in the future.</Paragraph>
  </Section>
class="xml-element"></Paper>