Comparing noun phrasing techniques for use with medical digital library tools

Kristin M. Tolle, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

98 Scopus citations


In an effort to assist medical researchers and professionals in accessing information necessary for their work, the A1 Lab at the University of Arizona is investigating the use of a natural language processing (NLP) technique called noun phrasing. The goal of this research is to determine whether noun phrasing could be a viable technique to include in medical information retrieval applications. Four noun phrase generation tools were evaluated as to their ability to isolate noun phrases from medical journal abstracts. Tests were conducted using the National Cancer Institute's CANCERLIT database. The NLP tools evaluated were Massachusetts Institute of Technology's (MIT's) Chopper, The University of Arizona's Automatic Indexer, Lingsoft's NPtool, and The University of Arizona's AZ Noun Phraser. In addition, the National Library of Medicine's SPECIALIST Lexicon was incorporated into two versions of the AZ Noun Phraser to be evaluated against the other tools as well as a nonaugmented version of the AZ Noun Phraser. Using the metrics relative subject recall and precision, our results show that, with the exception of Chopper, the phrasing tools were fairly comparable in recall and precision. It was also shown that augmenting the AZ Noun Phraser by including the SPECIALIST Lexicon from the National Library of Medicine resulted in improved recall and precision.

Original languageEnglish (US)
Pages (from-to)352-370
Number of pages19
JournalJournal of the American Society for Information Science and Technology
Issue number4
StatePublished - 2000

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications
  • Artificial Intelligence


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