Knowledge extraction from machine-readable dictionaries: An evaluation
Identifieur interne : 000202 ( Istex/Corpus ); précédent : 000201; suivant : 000203Knowledge extraction from machine-readable dictionaries: An evaluation
Auteurs : Nancy Ide ; Jean VéronisSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 1995.
Abstract
Abstract: Machine-readable versions of everyday dictionaries have been seen as a likely source of information for use in natural language processing because they contain an enormous amount of lexical and semantic knowledge. However, after 15 years of research, the results appear to be disappointing. No comprehensive evaluation of machine-readable dictionaries (MRDs) as a knowledge source has been made to date, although this is necessary to determine what, if anything, can be gained from MRD research. To this end, this paper will first consider the postulates upon which MRD research has been based over the past fifteen years, discuss the validity of these postulates, and evaluate the results of this work. We will then propose possible future directions and applications that may exploit these years of effort, in the light of current directions in not only NLP research, but also fields such as lexicography and electronic publishing.
Url:
DOI: 10.1007/3-540-59040-4_18
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ISTEX:7E40445436FA55E019FC991A19830840653296DALe document en format XML
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However, after 15 years of research, the results appear to be disappointing. No comprehensive evaluation of machine-readable dictionaries (MRDs) as a knowledge source has been made to date, although this is necessary to determine what, if anything, can be gained from MRD research. To this end, this paper will first consider the postulates upon which MRD research has been based over the past fifteen years, discuss the validity of these postulates, and evaluate the results of this work. 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However, after 15 years of research, the results appear to be disappointing. No comprehensive evaluation of machine-readable dictionaries (MRDs) as a knowledge source has been made to date, although this is necessary to determine what, if anything, can be gained from MRD research. To this end, this paper will first consider the postulates upon which MRD research has been based over the past fifteen years, discuss the validity of these postulates, and evaluate the results of this work. We will then propose possible future directions and applications that may exploit these years of effort, in the light of current directions in not only NLP research, but also fields such as lexicography and electronic publishing.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Knowledge extraction from machine-readable dictionaries: An evaluation</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Knowledge extraction from machine-readable dictionaries: An evaluation</title></titleInfo><name type="personal"><namePart type="given">Nancy</namePart><namePart type="family">Ide</namePart><affiliation>Laboratoire Parole et Langage, CNRS & Université de Provence, 29, Avenue Robert Schuman, 13621, Aix-en-Provence Cedex 1, France</affiliation><affiliation>E-mail: ide@fraix11.univ-aix.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jean</namePart><namePart type="family">Véronis</namePart><affiliation>Department or Computer Science, Vassar College, 12601, Poughkeepsie, New York, USA</affiliation><affiliation>E-mail: veronis@fraix11.univ-aix.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="review-article" displayLabel="ReviewPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005-06-04</dateIssued><copyrightDate encoding="w3cdtf">1995</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Machine-readable versions of everyday dictionaries have been seen as a likely source of information for use in natural language processing because they contain an enormous amount of lexical and semantic knowledge. 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