Extraction de motifs dialogiques bidimensionnels
Identifieur interne : 000244 ( France/Analysis ); précédent : 000243; suivant : 000245Extraction de motifs dialogiques bidimensionnels
Auteurs : Zacharie Alès [France] ; Alexandre Pauchet [France] ; Arnaud Knippel [France] ; Laurent Vercouter [France] ; Christian Gout [France]Source :
Descripteurs français
- mix :
Abstract
Cet article aborde le problème de l'extraction de régularités dans des dialogues sous la forme de motifs dialogiques. Nous présentons un algorithme de programmation dynamique en O(n3 ) permettant d'extraire de tableaux bidimensionnels d'annotations représentant des dialogues, des motifs récurrents. Cet algorithme, combiné à une méthode de clustering permet d'obtenir des régularités caractéristiques d'un corpus annoté. Les paramètres de la méthode sont évalués par tests statistiques.
Url:
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 000528
- to stream Hal, to step Curation: 000528
- to stream Hal, to step Checkpoint: 000130
- to stream Main, to step Merge: 000255
- to stream Main, to step Curation: 000255
- to stream Main, to step Exploration: 000255
- to stream France, to step Extraction: 000244
Links to Exploration step
Hal:hal-00989237Le document en format XML
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Rouen : EA3226</orgName><desc><address><country key="FR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>Extraction de régularités</term><term>modélisation du dialogue</term><term>modélisation du dialogue.</term><term>motifs dialogiques</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="fr">Cet article aborde le problème de l'extraction de régularités dans des dialogues sous la forme de motifs dialogiques. Nous présentons un algorithme de programmation dynamique en O(n3 ) permettant d'extraire de tableaux bidimensionnels d'annotations représentant des dialogues, des motifs récurrents. Cet algorithme, combiné à une méthode de clustering permet d'obtenir des régularités caractéristiques d'un corpus annoté. Les paramètres de la méthode sont évalués par tests statistiques.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Ales, Zacharie" sort="Ales, Zacharie" uniqKey="Ales Z" first="Zacharie" last="Alès">Zacharie Alès</name></region><name sortKey="Gout, Christian" sort="Gout, Christian" uniqKey="Gout C" first="Christian" last="Gout">Christian Gout</name><name sortKey="Knippel, Arnaud" sort="Knippel, Arnaud" uniqKey="Knippel A" first="Arnaud" last="Knippel">Arnaud Knippel</name><name sortKey="Pauchet, Alexandre" sort="Pauchet, Alexandre" uniqKey="Pauchet A" first="Alexandre" last="Pauchet">Alexandre Pauchet</name><name sortKey="Vercouter, Laurent" sort="Vercouter, Laurent" uniqKey="Vercouter L" first="Laurent" last="Vercouter">Laurent Vercouter</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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