Fast statistical parsing of noun phrases for document indexing

Research output: Contribution to conferencePaperpeer-review


Information Retrieval (IR) is an important application area of Natural Language Processing (NLP) where one encounters the genuine challenge of processing large quantities of unrestricted natural language text. While much effort has been made to apply NLP techniques to IR, very few NLP techniques have been evaluated on a document collection larger than several megabytes. Many NLP techniques are simply not efficient enough, and not robust enough, to handle a large amount of text. This paper proposes a new probabilistic model for noun phrase parsing, and reports on the application of such a parsing technique to enhance document indexing. The effectiveness of using syntactic phrases provided by the parser to supplement single words for indexing is evaluated with a 250 megabytes document collection. The experiment's results show that supplementing single words with syntactic phrases for indexing consistently and significantly improves retrieval performance.

Original languageEnglish (US)
Number of pages8
StatePublished - 1997
Externally publishedYes
Event5th Conference on Applied Natural Language Processing, ANLP 1997 - Washington, United States
Duration: Mar 31 1997Apr 3 1997


Conference5th Conference on Applied Natural Language Processing, ANLP 1997
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Software


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