  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.
