Comparing noun phrasing techniques for use with medical digital library tools
Identifieur interne : 004096 ( Istex/Corpus ); précédent : 004095; suivant : 004097Comparing noun phrasing techniques for use with medical digital library tools
Auteurs : Kristin M. Tolle ; Hsinchun ChenSource :
- Journal of the American Society for Information Science [ 0002-8231 ] ; 2000.
English descriptors
- Teeft :
- American society, Anick vaithyanathan, Annual symposium, Automatic indexing, Aznp, Boguraev, Boguraev pustejovski, Brill, Brill tagger, Brown corpus, Cambridge university press, Cancer research results, Cancerlit, Cancerlit collection, Chen, Chopper, Computational linguistics, Computer applications, Concept extraction, Concept space, Concept spaces, Current version, Data collections, Database, Detmer shortliffe, Different domains, Different phrases, Different techniques, Different versions, Digital libraries, Document collection, Document keywords, Document retrieval, Fewer phrases, Free text, Government reports, Great deal, High overlap, Human indexers, Human language technology, Hunting dogs, Indexer, Indexing, Information processing, Information producers, Information providers, Information retrieval, Information retrieval systems, Information science, Information seekers, Information sources, Interface, Interim noun phrases, Interim phrase generation tools, Interim phrases, Internet, Karlsson karttunen, Keyword, Keywords, Lexical, Lexical analysis, Lexical entries, Lexical knowledge, Lexical properties, Lexicon, Longest phrase, Longest phrase generation, Machine understanding group, Medical abstracts, Medical care, Medical domain, Medical goal, Medical information, Medical information retrieval, Medical journal abstracts, Medical professionals, Medical researchers, Medical terminology, Mesh terms, More phrases, National cancer institute, National library, Natural language, Natural language processing, Noun, Noun phrase, Noun phraser, Noun phraser noun phraser, Noun phraser versions, Noun phrasers, Noun phrases, Noun phrasing, Nptool, Nptool auto index, Other phrase generation techniques, Other techniques, Other tools, Overlap analysis, Patient record, Phrase, Phrase generation tools, Phrase output, Phraser, Phrasing, Phrasing tools, Precision comparison, Pustejovski, Query, Query expansion, Query formation, Relevant concepts, Relevant phrases, Research questions, Results show, Retrieval, Salton, Same document, Sample output, Schatz, Science alliance, Search engines, Semantic indexing, Semantic network, Semantic types, Sigir conference, Specialist lexicon, Statistical methods, Stochastic methods, Tagger, Test collection, Text corpora, Text documents, Textual data, Textual unit, Thesaurus, Total number, Training corpus, Umls, Umls knowledge sources, Unbiased estimates, Variance, Vector space representation, Vocabulary problem, Wall street journal.
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.
Url:
DOI: 10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.0.CO;2-8
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Tolle</name><affiliations><json:string>E-mail: ktolle@bpa.arizona.edu</json:string><json:string>Management Information Systems Department, University of Arizona, Tucson, AZ 85721</json:string><json:string>E-mail: ktolle@bpa.arizona.edu</json:string></affiliations></json:item><json:item><name>Hsinchun Chen</name><affiliations><json:string>Management Information Systems Department, University of Arizona, Tucson, AZ 85721</json:string></affiliations></json:item></author><articleId><json:string>ASI5</json:string></articleId><arkIstex>ark:/67375/WNG-RRGVX20X-M</arkIstex><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><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. 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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.</p></abstract><textClass><keywords rend="articleCategory"><term>Research Article</term></keywords><keywords rend="tocHeading1"><term>Research Article</term></keywords></textClass><textClass><keywords ana="subject"><term ref="psi.asis.org/digital/nouns">nouns</term><term ref="psi.asis.org/digital/phrases">phrases</term><term ref="psi.asis.org/digital/biomedical+information">biomedical information</term><term ref="psi.asis.org/digital/recall">recall</term><term ref="psi.asis.org/digital/natural+language+processing">natural language processing</term><term ref="psi.asis.org/digital/precision">precision</term><term ref="psi.asis.org/digital/medical+libraries">medical libraries</term><term ref="psi.asis.org/digital/digital+libraries">digital libraries</term></keywords></textClass><langUsage><language ident="en"></language></langUsage></profileDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/ark:/67375/WNG-RRGVX20X-M/fulltext.txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>John Wiley & Sons, Inc.</publisherName><publisherLoc>New York</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1097-4571</doi><issn type="print">0002-8231</issn><issn type="electronic">1097-4571</issn><idGroup><id type="product" value="ASI"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE">Journal of the American Society for Information Science</title><title type="short">J. 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Sci.</title></titleGroup></publicationMeta><publicationMeta level="part" position="40"><doi origin="wiley" registered="yes">10.1002/(SICI)1097-4571(2000)51:4<>1.0.CO;2-B</doi><titleGroup><title type="specialIssueTitle">Digital Libraries: Part 2</title></titleGroup><numberingGroup><numbering type="journalVolume" number="51">51</numbering><numbering type="journalIssue">4</numbering></numberingGroup><creators><creator xml:id="sped1" creatorRole="sponsoringEditor" affiliationRef="#sp1"><personName><givenNames>Hsinchun</givenNames><familyName>Chen</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="sp1" countryCode="US" type="organization"><unparsedAffiliation>McClelland Professor of MIS, Artificial Intelligence Lab, Management Information Systems Department, The University of Arizona, Tucson, AZ 85721</unparsedAffiliation></affiliation></affiliationGroup><coverDate startDate="2000">2000</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="5" status="forIssue"><doi origin="wiley" registered="yes">10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.0.CO;2-8</doi><idGroup><id type="unit" value="ASI5"></id></idGroup><countGroup><count type="pageTotal" number="19"></count></countGroup><titleGroup><title type="articleCategory">Research Article</title><title type="tocHeading1">Research Article</title></titleGroup><copyright ownership="publisher">Copyright © 2000 John Wiley & Sons, Inc.</copyright><eventGroup><event type="firstOnline" date="2000-02-11"></event><event type="publishedOnlineFinalForm" date="2000-02-11"></event><event type="xmlConverted" agent="Converter:JWSART34_TO_WML3G version:2.4.7 mode:FullText source:FullText result:FullText mathml2tex" date="2011-02-24"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-06"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-30"></event></eventGroup><numberingGroup><numbering type="pageFirst">352</numbering><numbering type="pageLast">370</numbering></numberingGroup><subjectInfo><subject href="psi.asis.org/digital/nouns">nouns</subject><subject href="psi.asis.org/digital/phrases">phrases</subject><subject href="psi.asis.org/digital/biomedical+information">biomedical information</subject><subject href="psi.asis.org/digital/recall">recall</subject><subject href="psi.asis.org/digital/natural+language+processing">natural language processing</subject><subject href="psi.asis.org/digital/precision">precision</subject><subject href="psi.asis.org/digital/medical+libraries">medical libraries</subject><subject href="psi.asis.org/digital/digital+libraries">digital libraries</subject></subjectInfo><linkGroup><link type="toTypesetVersion" href="file:ASI.ASI5.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="13"></count><count type="tableTotal" number="5"></count><count type="referenceTotal" number="49"></count><count type="wordTotal" number="10560"></count></countGroup><titleGroup><title type="main" xml:lang="en">Comparing noun phrasing techniques for use with medical digital library tools</title></titleGroup><creators><creator xml:id="au1" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Kristin M.</givenNames><familyName>Tolle</familyName></personName><contactDetails><email>ktolle@bpa.arizona.edu</email></contactDetails></creator><creator xml:id="au2" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Hsinchun</givenNames><familyName>Chen</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="af1" countryCode="US" type="organization"><unparsedAffiliation>Management Information Systems Department, University of Arizona, Tucson, AZ 85721</unparsedAffiliation></affiliation></affiliationGroup><fundingInfo><fundingAgency>NSF/ARPA/NASA Digital Library Initiative</fundingAgency><fundingNumber>IRI‐9411318</fundingNumber></fundingInfo><fundingInfo><fundingAgency>NSF CISE</fundingAgency><fundingNumber>IRI‐9525790</fundingNumber></fundingInfo><fundingInfo><fundingAgency>National Computational Science Alliance (NCSA)</fundingAgency><fundingNumber>IRI970000N</fundingNumber><fundingNumber>IRI970002N</fundingNumber></fundingInfo><fundingInfo><fundingAgency>National Library of Medicine (NLM)</fundingAgency></fundingInfo><fundingInfo><fundingAgency>National Cancer Institute</fundingAgency></fundingInfo><fundingInfo><fundingAgency>National Institutes of Health</fundingAgency></fundingInfo><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>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. 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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.</abstract><note type="funding">NSF/ARPA/NASA Digital Library Initiative - No. IRI‐9411318; </note><note type="funding">NSF CISE - No. IRI‐9525790; </note><note type="funding">National Computational Science Alliance (NCSA) - No. IRI970000N; No. IRI970002N; </note><note type="funding">National Library of Medicine (NLM)</note><note type="funding">National Cancer Institute</note><note type="funding">National Institutes of Health</note><relatedItem type="host"><titleInfo><title>Journal of the American Society for Information Science</title></titleInfo><titleInfo type="abbreviated"><title>J. Am. Soc. Inf. Sci.</title></titleInfo><name type="personal"><namePart type="given">Hsinchun</namePart><namePart type="family">Chen</namePart><affiliation>McClelland Professor of MIS, Artificial Intelligence Lab, Management Information Systems Department, The University of Arizona, Tucson, AZ 85721</affiliation></name><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><subject><genre>index-terms</genre><topic authorityURI="psi.asis.org/digital/nouns">nouns</topic><topic authorityURI="psi.asis.org/digital/phrases">phrases</topic><topic authorityURI="psi.asis.org/digital/biomedical+information">biomedical information</topic><topic authorityURI="psi.asis.org/digital/recall">recall</topic><topic authorityURI="psi.asis.org/digital/natural+language+processing">natural language processing</topic><topic authorityURI="psi.asis.org/digital/precision">precision</topic><topic authorityURI="psi.asis.org/digital/medical+libraries">medical libraries</topic><topic authorityURI="psi.asis.org/digital/digital+libraries">digital libraries</topic></subject><subject><genre>article-category</genre><topic>Research Article</topic></subject><identifier type="ISSN">0002-8231</identifier><identifier type="eISSN">1097-4571</identifier><identifier type="DOI">10.1002/(ISSN)1097-4571</identifier><identifier type="PublisherID">ASI</identifier><part><date>2000</date><detail type="title"><title>Digital Libraries: Part 2</title></detail><detail type="volume"><caption>vol.</caption><number>51</number></detail><detail type="issue"><caption>no.</caption><number>4</number></detail><extent unit="pages"><start>352</start><end>370</end><total>19</total></extent></part></relatedItem><identifier type="istex">F89A78E6F114B4601DE09E6221D5DF8DC95B240F</identifier><identifier type="ark">ark:/67375/WNG-RRGVX20X-M</identifier><identifier type="DOI">10.1002/(SICI)1097-4571(2000)51:4<352::AID-ASI5>3.0.CO;2-8</identifier><identifier type="ArticleID">ASI5</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2000 John Wiley & Sons, Inc.</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-L0C46X92-X">wiley</recordContentSource><recordOrigin>John Wiley & Sons, Inc.</recordOrigin></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/ark:/67375/WNG-RRGVX20X-M/record.json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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