Improving Document Transformation Techniques with Collaborative Learned Term-Based Concepts
Identifieur interne : 001164 ( Istex/Corpus ); précédent : 001163; suivant : 001165Improving Document Transformation Techniques with Collaborative Learned Term-Based Concepts
Auteurs : Stefan KlinkSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2004.
Abstract
Abstract: Document Transformation techniques have been studied for decades. In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.
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DOI: 10.1007/978-3-540-24642-8_16
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In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Stefan Klink</name><affiliations><json:string>Department of Database and Information Systems (DBIS), University of Trier, D–54286, Trier, Germany</json:string><json:string>E-mail: klink@uni-trier.de</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><originalGenre><json:string>OriginalPaper</json:string></originalGenre><abstract>Abstract: Document Transformation techniques have been studied for decades. In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. 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xml:id="author-1"><persName><forename type="first">Stefan</forename><surname>Klink</surname></persName><email>klink@uni-trier.de</email><affiliation>Department of Database and Information Systems (DBIS), University of Trier, D–54286, Trier, Germany</affiliation></author></analytic><monogr><title level="m">Reading and Learning</title><title level="m" type="sub">Adaptive Content Recognition</title><idno type="pISBN">978-3-540-21904-0</idno><idno type="eISBN">978-3-540-24642-8</idno><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="DOI">10.1007/b96988</idno><idno type="book-ID">978-3-540-24642-8</idno><idno type="book-title-ID">82308</idno><idno type="book-sequence-number">2956</idno><idno type="book-volume-number">2956</idno><idno type="book-chapter-count">18</idno><editor><persName><forename type="first">Andreas</forename><surname>Dengel</surname></persName><email>Andreas.Dengel@dfki.de</email><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany</affiliation></editor><editor><persName><forename type="first">Markus</forename><surname>Junker</surname></persName><email>Markus.Junker@dfki.de</email><affiliation>German Research Center for Artificial Intelligence (DFKI), P.O.Box 2080, 67608, Kaiserslautern, Germany</affiliation></editor><editor><persName><forename type="first">Anette</forename><surname>Weisbecker</surname></persName><email>Anette.Weisbecker@iao.fraunhofer.de</email><affiliation>Institute for Human Factors and Technology Management, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany</affiliation></editor><imprint><publisher>Springer Berlin Heidelberg</publisher><pubPlace>Berlin, Heidelberg</pubPlace><date type="published" when="2004"></date><biblScope unit="volume">2956</biblScope><biblScope unit="page" from="281">281</biblScope><biblScope unit="page" to="305">305</biblScope></imprint></monogr><series><title level="s">Lecture Notes in Computer Science</title><editor><persName><forename type="first">Gerhard</forename><surname>Goos</surname></persName><affiliation>Karlsruhe University, Germany</affiliation></editor><editor><persName><forename type="first">Juris</forename><surname>Hartmanis</surname></persName><affiliation>Cornell University, NY, USA</affiliation></editor><editor><persName><forename type="first">Jan</forename><surname>van Leeuwen</surname></persName><affiliation>Utrecht University, The Netherlands</affiliation></editor><biblScope><date>2004</date></biblScope><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="series-Id">558</idno></series><idno type="istex">7D9162C16271F64FEF1323B1637F84123B22E0D2</idno><idno type="DOI">10.1007/978-3-540-24642-8_16</idno><idno type="ChapterID">16</idno><idno type="ChapterID">Chap16</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2004</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Abstract: Document Transformation techniques have been studied for decades. In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.</p></abstract><textClass><keywords scheme="Book-Subject-Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book-Subject-Group"><list><label>I</label><label>I2203X</label><label>I18032</label><label>I18040</label><label>I21017</label><label>I21033</label><label>I22021</label><item><term>Computer Science</term></item><item><term>Pattern Recognition</term></item><item><term>Information Storage and Retrieval</term></item><item><term>Information Systems Applications (incl.Internet)</term></item><item><term>Artificial Intelligence (incl. Robotics)</term></item><item><term>Document Preparation and Text Processing</term></item><item><term>Image Processing and Computer Vision</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2004">Published</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-11-22">References added</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2017-01-20">References added</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/7D9162C16271F64FEF1323B1637F84123B22E0D2/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer Berlin Heidelberg</PublisherName><PublisherLocation>Berlin, Heidelberg</PublisherLocation></PublisherInfo><Series><SeriesInfo SeriesType="Series" TocLevels="0"><SeriesID>558</SeriesID><SeriesPrintISSN>0302-9743</SeriesPrintISSN><SeriesElectronicISSN>1611-3349</SeriesElectronicISSN><SeriesTitle Language="En">Lecture Notes in Computer Science</SeriesTitle></SeriesInfo><SeriesHeader><EditorGroup><Editor AffiliationIDS="Aff1"><EditorName DisplayOrder="Western"><GivenName>Gerhard</GivenName><FamilyName>Goos</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff2"><EditorName DisplayOrder="Western"><GivenName>Juris</GivenName><FamilyName>Hartmanis</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff3"><EditorName DisplayOrder="Western"><GivenName>Jan</GivenName><Particle>van</Particle><FamilyName>Leeuwen</FamilyName></EditorName></Editor><Affiliation ID="Aff1"><OrgName>Karlsruhe University</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff2"><OrgName>Cornell University</OrgName><OrgAddress><State>NY</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff3"><OrgName>Utrecht University</OrgName><OrgAddress><Country>The Netherlands</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-24642-8</BookID><BookTitle>Reading and Learning</BookTitle><BookSubTitle>Adaptive Content Recognition</BookSubTitle><BookVolumeNumber>2956</BookVolumeNumber><BookSequenceNumber>2956</BookSequenceNumber><BookDOI>10.1007/b96988</BookDOI><BookTitleID>82308</BookTitleID><BookPrintISBN>978-3-540-21904-0</BookPrintISBN><BookElectronicISBN>978-3-540-24642-8</BookElectronicISBN><BookChapterCount>18</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2004</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I18032" Priority="2" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18040" Priority="3" Type="Secondary">Information Systems Applications (incl.Internet)</BookSubject><BookSubject Code="I21017" Priority="4" Type="Secondary">Artificial Intelligence (incl. 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In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Improving Document Transformation Techniques with Collaborative Learned Term-Based Concepts</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Improving Document Transformation Techniques with Collaborative Learned Term-Based Concepts</title></titleInfo><name type="personal"><namePart type="given">Stefan</namePart><namePart type="family">Klink</namePart><affiliation>Department of Database and Information Systems (DBIS), University of Trier, D–54286, Trier, Germany</affiliation><affiliation>E-mail: klink@uni-trier.de</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2004</dateIssued><copyrightDate encoding="w3cdtf">2004</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: Document Transformation techniques have been studied for decades. In this paper, a new approach for a significant improvement is presented based on using a new query expansion method. In contrast to other methods, the regarded query is expanded by adding those terms that are most similar to the concept of individual query terms, rather than selecting terms that are similar to the complete query or that are directly similar to the query terms. Experiments have shown that Document Transformation techniques are significantly improved in the retrieval effectiveness when measuring the recall-precision.</abstract><relatedItem type="host"><titleInfo><title>Reading and Learning</title><subTitle>Adaptive Content Recognition</subTitle></titleInfo><name type="personal"><namePart type="given">Andreas</namePart><namePart type="family">Dengel</namePart><affiliation>Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Kaiserslautern, Germany</affiliation><affiliation>E-mail: Andreas.Dengel@dfki.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Markus</namePart><namePart type="family">Junker</namePart><affiliation>German Research Center for Artificial Intelligence (DFKI), P.O.Box 2080, 67608, Kaiserslautern, Germany</affiliation><affiliation>E-mail: Markus.Junker@dfki.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Anette</namePart><namePart type="family">Weisbecker</namePart><affiliation>Institute for Human Factors and Technology Management, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany</affiliation><affiliation>E-mail: Anette.Weisbecker@iao.fraunhofer.de</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2004</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I18040">Information Systems Applications (incl.Internet)</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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