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<Paper uid="P00-1015">
  <Title>A Unified Statistical Model for the Identification of English BaseNP</Title>
  <Section position="7" start_page="121" end_page="121" type="concl">
    <SectionTitle>
5 Conclusions
</SectionTitle>
    <Paragraph position="0"> This paper presented a unified statistical model to identify baseNP in English text. Compared with other methods, our approach has following characteristics: (1) baseNP identification is implemented in two related stages: N-best POS taggings are first determined, then baseNPs are identified given the N best POS-sequences. Unlike other approaches that use POS tagging as preprocessing, our approach is not dependant on perfect POS-tagging, Moreover, we can apply baseNP information to further increase the precision of POS tagging can be improved.</Paragraph>
    <Paragraph position="1"> These experiments triggered an interesting future research challenge: how to cluster certain baseNP rules into certain identifiers so as to improve the precision of both baseNP and POS tagging. This is one of our further research topics.</Paragraph>
    <Paragraph position="2"> (2) Our statistical model makes use of more lexical information than other approaches. Every word in the sentence is taken into account during baseNP identification.</Paragraph>
    <Paragraph position="3"> (3) Viterbi algorithm is applied to make global search at the sentence level.</Paragraph>
    <Paragraph position="4"> Experiment with the same testing data used by the other methods showed that the precision is 92.3% and the recall is 93.2%. To our knowledge, these results are comparable with or better than all previously reported results.</Paragraph>
  </Section>
class="xml-element"></Paper>