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<Paper uid="P06-2011">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A High-Accurate Chinese-English NE Backward Translation System Combining Both Lexical Information and Web Statistics</Title>
  <Section position="7" start_page="87" end_page="87" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> In this study we combine several relatively simple implementations of approaches that have been proposed in the previous studies and obtain a very good performance. We find that the Internet is a quite good source for discovering NE translations. Using snippets returned by Google we can efficiently reduce the number of the possible candidates and acquire much useful information to verify these candidates. Since the number of candidates is generally less than processing with unaligned corpus, simple models can performs filtering quite well and the over-fitting problem is thus prevented.</Paragraph>
    <Paragraph position="1"> From the failure cases of our system, (see Appendix A) we could observe that the performance of this integrated approach could still be boosted by more sophisticated models, more extensive dictionaries, and more delicate training mechanisms. For example, performing stemming or adopting a more extensive dictionary might enhance the accuracy of estimating word sense similarity; the statistic formula can be replaced by more formal measures such as co-occurrences or mutual information to make a more precise assessment of statistical relationship. These tasks would be our future works in developing a more accurate and efficient NE translation system.</Paragraph>
  </Section>
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