Rethinking Language Models Within the Framework of Dynamic Bayesian Networks
Identifieur interne : 003731 ( Istex/Corpus ); précédent : 003730; suivant : 003732Rethinking Language Models Within the Framework of Dynamic Bayesian Networks
Auteurs : Murat Deviren ; Khalid Daoudi ; Kamel SmaïliSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: We present a new approach for language modeling based on dynamic Bayesian networks. The philosophy behind this architecture is to learn from data the appropriate relations of dependency between the linguistic variables used in language modeling process. It is an original and coherent framework that processes words and classes in the same model. This approach leads to new data-driven language models capable of outperforming classical ones, sometimes with lower computational complexity. We present experiments on a small and medium corpora. The results show that this new technique is very promising and deserves further investigations.
Url:
DOI: 10.1007/11424918_47
Links to Exploration step
ISTEX:E692E17DEBBF885A77D0F89D43D06FFFB7377D88Le document en format XML
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ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesPrintISSN>0302-9743</SubSeriesPrintISSN><SubSeriesElectronicISSN>1611-3349</SubSeriesElectronicISSN><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Jaime</GivenName><GivenName>G.</GivenName><FamilyName>Carbonell</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Affiliation ID="Aff16"><OrgName>Carnegie Mellon University</OrgName><OrgAddress><City>Pittsburgh</City><State>PA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-31952-8</BookID><BookTitle>Advances in Artificial Intelligence</BookTitle><BookSubTitle>18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9-11, 2005. Proceedings</BookSubTitle><BookVolumeNumber>3501</BookVolumeNumber><BookSequenceNumber>3501</BookSequenceNumber><BookDOI>10.1007/b136277</BookDOI><BookTitleID>118129</BookTitleID><BookPrintISBN>978-3-540-25864-3</BookPrintISBN><BookElectronicISBN>978-3-540-31952-8</BookElectronicISBN><BookChapterCount>49</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I21017" Priority="1" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID><SubSeriesID>1244</SubSeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Balázs</GivenName><FamilyName>Kégl</FamilyName></EditorName><Contact><Email>kegl@iro.umontreal.ca</Email></Contact></Editor><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Guy</GivenName><FamilyName>Lapalme</FamilyName></EditorName><Contact><Email>lapalme@iro.umontreal.ca</Email></Contact></Editor><Affiliation ID="Aff18"><OrgDivision>Département d’informatique et de recherche opérationelle, CP 6128 succ. Centre-Ville</OrgDivision><OrgName>Université de Montréal</OrgName><OrgAddress><Postcode>H3C 3J7</Postcode><City>Montréal</City><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff19"><OrgDivision>Département d’informatique et de recherche opérationnelle</OrgDivision><OrgName>Université de Montréal</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part6"><PartInfo TocLevels="0"><PartID>6</PartID><PartSequenceNumber>6</PartSequenceNumber><PartTitle>Natural Language</PartTitle><PartChapterCount>15</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Advances in Artificial Intelligence</BookTitle></PartContext></PartInfo><Chapter ID="Chap47" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>47</ChapterID><ChapterDOI>10.1007/11424918_47</ChapterDOI><ChapterSequenceNumber>47</ChapterSequenceNumber><ChapterTitle Language="En">Rethinking Language Models Within the Framework of Dynamic Bayesian Networks</ChapterTitle><ChapterFirstPage>432</ChapterFirstPage><ChapterLastPage>437</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>6</PartID><BookID>978-3-540-31952-8</BookID><BookTitle>Advances in Artificial Intelligence</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Murat</GivenName><FamilyName>Deviren</FamilyName></AuthorName><Contact><Email>daoudi@loria.fr</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Khalid</GivenName><FamilyName>Daoudi</FamilyName></AuthorName><Contact><Email>deviren@loria.fr</Email></Contact></Author><Author AffiliationIDS="Aff20"><AuthorName DisplayOrder="Western"><GivenName>Kamel</GivenName><FamilyName>Smaïli</FamilyName></AuthorName><Contact><Email>smaili@loria.fr</Email></Contact></Author><Affiliation ID="Aff20"><OrgDivision>Parole team</OrgDivision><OrgName>INRIA-LORIA</OrgName><OrgAddress><Postcode>54602</Postcode><City>Villers les Nancy</City><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>We present a new approach for language modeling based on dynamic Bayesian networks. The philosophy behind this architecture is to learn from data the appropriate relations of dependency between the linguistic variables used in language modeling process. It is an original and coherent framework that processes words and classes in the same model. This approach leads to new data-driven language models capable of outperforming classical ones, sometimes with lower computational complexity. We present experiments on a small and medium corpora. The results show that this new technique is very promising and deserves further investigations.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Rethinking Language Models Within the Framework of Dynamic Bayesian Networks</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Rethinking Language Models Within the Framework of Dynamic Bayesian Networks</title></titleInfo><name type="personal"><namePart type="given">Murat</namePart><namePart type="family">Deviren</namePart><affiliation>Parole team, INRIA-LORIA, 54602, Villers les Nancy, France</affiliation><affiliation>E-mail: daoudi@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Khalid</namePart><namePart type="family">Daoudi</namePart><affiliation>Parole team, INRIA-LORIA, 54602, Villers les Nancy, France</affiliation><affiliation>E-mail: deviren@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Kamel</namePart><namePart type="family">Smaïli</namePart><affiliation>Parole team, INRIA-LORIA, 54602, Villers les Nancy, France</affiliation><affiliation>E-mail: smaili@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: We present a new approach for language modeling based on dynamic Bayesian networks. The philosophy behind this architecture is to learn from data the appropriate relations of dependency between the linguistic variables used in language modeling process. It is an original and coherent framework that processes words and classes in the same model. This approach leads to new data-driven language models capable of outperforming classical ones, sometimes with lower computational complexity. We present experiments on a small and medium corpora. The results show that this new technique is very promising and deserves further investigations.</abstract><relatedItem type="host"><titleInfo><title>Advances in Artificial Intelligence</title><subTitle>18th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2005, Victoria, Canada, May 9-11, 2005. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Balázs</namePart><namePart type="family">Kégl</namePart><affiliation>Département d’informatique et de recherche opérationelle, CP 6128 succ. Centre-Ville, Université de Montréal, H3C 3J7, Montréal, Canada</affiliation><affiliation>E-mail: kegl@iro.umontreal.ca</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Guy</namePart><namePart type="family">Lapalme</namePart><affiliation>Département d’informatique et de recherche opérationnelle, Université de Montréal, </affiliation><affiliation>E-mail: lapalme@iro.umontreal.ca</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2005</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="I21017">Artificial Intelligence (incl. Robotics)</topic></subject><identifier type="DOI">10.1007/b136277</identifier><identifier type="ISBN">978-3-540-25864-3</identifier><identifier type="eISBN">978-3-540-31952-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">118129</identifier><identifier type="BookID">978-3-540-31952-8</identifier><identifier type="BookChapterCount">49</identifier><identifier type="BookVolumeNumber">3501</identifier><identifier type="BookSequenceNumber">3501</identifier><identifier type="PartChapterCount">15</identifier><part><date>2005</date><detail type="part"><title>Natural Language</title></detail><detail type="volume"><number>3501</number><caption>vol.</caption></detail><extent unit="pages"><start>432</start><end>437</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 2005</recordOrigin></recordInfo></relatedItem><relatedItem type="series"><titleInfo><title>Lecture Notes in Computer 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Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>New York University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dough</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, 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