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<Paper uid="A97-1046">
  <Title>Fast Statistical Parsing of Noun Phrases for Document Indexing</Title>
  <Section position="9" start_page="316" end_page="317" type="concl">
    <SectionTitle>
6 Conclusions
</SectionTitle>
    <Paragraph position="0"> Information retrieval provides a good way to quantitatively (although indirectly) evaluate various NLP techniques. We explored the application of a fast statistical noun phrase parser to enhance document indexing in information retrieval. We proposed a new probabilistic model for noun phrase parsing and developed a fast noun phrase parser that can handle relatively large amounts of text efficiently. The effectiveness of enhancing document indexing with the syntactic phrases provided by the noun phrase parser was evaluated on the Wall Street Journal database in Tipster Disk2 using 50 TREC-5 ad hoc topics. Experiment results on this 250-megabyte document collection have shown that using different kinds of syntactic phrases provided by the noun phrase parser to supplement single words for indexing can significantly improve the retrieval performance, which is more encouraging than many early experiments on syntactic phrase indexing. Thus, using selective NLP, such as the noun phrase parsing technique we proposed, is not only feasible for use in information retrieval, but also effective in enhancing the retrieval performance./deg 1degWhether such syntactic phrases are more effective than simple statistical phrases (e.g., high frequency word  There are two lines of future work: First, the results from information retrieval experiments often show variances on different kinds of document collections and different sizes of collections. It is thus desirable to test the noun phrase parsing technique in other and larger collections.</Paragraph>
    <Paragraph position="1"> More experiments and analyses are also needed to better understand how to more effectively combine different phrases with single words. In addition, it is very important to study how such phrase effects interact with other useful IR techniques such as relevancy feedback, query expansion, and term weighting. null Second, it is desirable to study how the parsing quality (e.g., in terms of the ratio of phrases parsed correctly) would affect the retrieval performance. It is very interesting to try the conditional probability model as mentioned in a footnote in section 3 The improvement of the probabilistic model of noun phrase parsing may result in phrases of higher quality than the phrases produced by the current noun phrase parser. Intuitively, the use of higher quality phrases might enhance document indexing more effectively, but this again needs to be tested.</Paragraph>
  </Section>
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