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<Paper uid="W06-0112">
  <Title>Sydney, July 2006. c(c)2006 Association for Computational Linguistics A Hybrid Approach to Chinese Base Noun Phrase Chunking</Title>
  <Section position="7" start_page="91" end_page="91" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> This paper presented a new hybrid approach for identifying the Chinese base NPs. Our hybrid approach uses the SVM and CRF algorithm to design the preliminary classifiers for chunking.</Paragraph>
    <Paragraph position="1"> Furthermore with the direct comparison between the results from the former chunkers, we figure out that those two statistical methods are myopic about the compact chunking data of sequential noun. With the intention of capturing the syntactic dependence within those sequential chunking data, we make use of the conditional probabilities of the chunking tags given the corresponding tokens derived from CRF and some simple grammar rules to modify the preliminary results.</Paragraph>
    <Paragraph position="2"> The overall results achieve 89.27% precision on the base NP chunking. We attempt to explain some existing semantic problems and solve a certain part of problems, which have been discovered and explained in the paper. Future work will concentrate on working out some adaptive machine learning methods to make grammar rules automatically, select better features and employ suitable classifiers for Chinese base NP chunking. Finally, the particular Chinese base phrase grammars need a complete study, and the approach provides a primary solution and framework to process an analyses and comparisons between Chinese and English parallel base NP chunkers.</Paragraph>
  </Section>
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