Comparing noun phrasing techniques for use with medical digital library tools

Persistent Link:
http://hdl.handle.net/10150/105749
Title:
Comparing noun phrasing techniques for use with medical digital library tools
Author:
Tolle, Kristin M.; Chen, Hsinchun
Citation:
Comparing noun phrasing techniques for use with medical digital library tools 2000-02, 51(4):352-370 Journal of the American Society for Information Science
Publisher:
EBSCO
Journal:
Journal of the American Society for Information Science
Issue Date:
Feb-2000
Description:
Artificial Intelligence Lab, Department of MIS, Univeristy of Arizona
URI:
http://hdl.handle.net/10150/105749
Submitted date:
2004-08-13
Abstract:
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.
Type:
Journal (Paginated)
Language:
en
Keywords:
Evaluation; Medical Libraries; Digital Libraries
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Natural language processing; CANCERLIT

Full metadata record

DC FieldValue Language
dc.contributor.authorTolle, Kristin M.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-08-13T00:00:01Z-
dc.date.available2010-06-18T23:33:47Z-
dc.date.issued2000-02en_US
dc.date.submitted2004-08-13en_US
dc.identifier.citationComparing noun phrasing techniques for use with medical digital library tools 2000-02, 51(4):352-370 Journal of the American Society for Information Scienceen_US
dc.identifier.urihttp://hdl.handle.net/10150/105749-
dc.descriptionArtificial Intelligence Lab, Department of MIS, Univeristy of Arizonaen_US
dc.description.abstractIn 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.en_US
dc.format.mimetypetext/htmlen_US
dc.language.isoenen_US
dc.publisherEBSCOen_US
dc.subjectEvaluationen_US
dc.subjectMedical Librariesen_US
dc.subjectDigital Librariesen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherNatural language processingen_US
dc.subject.otherCANCERLITen_US
dc.titleComparing noun phrasing techniques for use with medical digital library toolsen_US
dc.typeJournal (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Scienceen_US
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