  Information retrieval is an important application area of natural-language
processing where one encounters the genuine challenge of processing large
quantities of unrestricted natural-language text. This paper reports on the
application of a few simple, yet robust and efficient noun-phrase analysis
techniques to create better indexing phrases for information retrieval. In
particular, we describe a hybrid approach to the extraction of meaningful
(continuous or discontinuous) subcompounds from complex noun phrases using both
corpus statistics and linguistic heuristics. Results of experiments show that
indexing based on such extracted subcompounds improves both recall and
precision in an information retrieval system. The noun-phrase analysis
techniques are also potentially useful for book indexing and automatic
thesaurus extraction.
