Understanding speech based on a Bayesian concept extraction method
Identifieur interne : 005035 ( Hal/Corpus ); précédent : 005034; suivant : 005036Understanding speech based on a Bayesian concept extraction method
Auteurs : Salma Jamoussi ; Kamel Smaïli ; Jean-Paul HatonSource :
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Abstract
The automatic speech understanding problem could be considered as an association problem between two different languages. At the entry, the query expressed in oral or written natural language and at the end, just before the interpretation stage, the same request is expressed in term of concepts. One concept represents a given meaning, it is defined by a set of words sharing the same semantic properties. In this paper, we propose a new Bayesian network based method to automatically extract the underlined concepts. We also propose three different approaches for the vector representation of words. This representation allows the Bayesian network to build the adequate list of concepts for the considered application. This step is very important to obtain well built concepts. We finish this paper by a description of the post-processing step during which, we label our sentences and we generate the corresponding SQL queries. This step allows us to validate our automatic understanding approach and to obtain 92.5 of correct SQL queries on the test corpus.
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At the entry, the query expressed in oral or written natural language and at the end, just before the interpretation stage, the same request is expressed in term of concepts. One concept represents a given meaning, it is defined by a set of words sharing the same semantic properties. In this paper, we propose a new Bayesian network based method to automatically extract the underlined concepts. We also propose three different approaches for the vector representation of words. This representation allows the Bayesian network to build the adequate list of concepts for the considered application. This step is very important to obtain well built concepts. We finish this paper by a description of the post-processing step during which, we label our sentences and we generate the corresponding SQL queries. This step allows us to validate our automatic understanding approach and to obtain 92.5 of correct SQL queries on the test corpus.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Understanding speech based on a Bayesian concept extraction method</title><author role="aut"><persName><forename type="first">Salma</forename><surname>Jamoussi</surname></persName><email>jamoussi@loria.fr</email><idno type="halauthor">130942</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Kamel</forename><surname>Smaïli</surname></persName><email></email><idno type="idhal">kamel-smaili</idno><idno type="halauthor">188841</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Paul</forename><surname>Haton</surname></persName><email>Jean-Paul.Haton@loria.fr</email><idno type="halauthor">130114</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 09:40:24</date><date type="whenModified">2016-05-18 08:59:37</date><date type="whenReleased">2006-09-28 15:22:46</date><date type="whenProduced">2003-09</date></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00099696</idno><idno type="halUri">https://hal.inria.fr/inria-00099696</idno><idno type="halBibtex">jamoussi:inria-00099696</idno><idno type="halRefHtml">Sixth International Conference on Text Speech and Dialogue - TSD'03, Sep 2003, Ceské-Budejovic, République Tchèque, 8 p, 2003</idno><idno type="halRef">Sixth International Conference on Text Speech and Dialogue - TSD'03, Sep 2003, Ceské-Budejovic, République Tchèque, 8 p, 2003</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes et comité de lecture. Internationale.</note><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Understanding speech based on a Bayesian concept extraction method</title><author role="aut"><persName><forename type="first">Salma</forename><surname>Jamoussi</surname></persName><email>jamoussi@loria.fr</email><idno type="halAuthorId">130942</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Kamel</forename><surname>Smaïli</surname></persName><idno type="idHal">kamel-smaili</idno><idno type="halAuthorId">188841</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Jean-Paul</forename><surname>Haton</surname></persName><email>Jean-Paul.Haton@loria.fr</email><idno type="halAuthorId">130114</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><idno type="localRef">A03-R-415 || jamoussi03b</idno><meeting><title>Sixth International Conference on Text Speech and Dialogue - TSD'03</title><date type="start">2003-09</date><settlement>Ceské-Budejovic, République Tchèque</settlement></meeting><imprint><biblScope unit="pp">8 p</biblScope><date type="datePub">2003-09</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">compréhension de la parole</term><term xml:lang="fr">semantic concepts</term><term xml:lang="fr">speech understanding</term><term xml:lang="fr">bayesian classification</term><term xml:lang="fr">semantic word representation</term><term xml:lang="fr">classification bayésienne</term><term xml:lang="fr">représentation semantique des mots</term><term xml:lang="fr">concepts sémantiques</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">The automatic speech understanding problem could be considered as an association problem between two different languages. At the entry, the query expressed in oral or written natural language and at the end, just before the interpretation stage, the same request is expressed in term of concepts. One concept represents a given meaning, it is defined by a set of words sharing the same semantic properties. In this paper, we propose a new Bayesian network based method to automatically extract the underlined concepts. We also propose three different approaches for the vector representation of words. This representation allows the Bayesian network to build the adequate list of concepts for the considered application. This step is very important to obtain well built concepts. We finish this paper by a description of the post-processing step during which, we label our sentences and we generate the corresponding SQL queries. This step allows us to validate our automatic understanding approach and to obtain 92.5 of correct SQL queries on the test corpus.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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