Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/109443
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dc.contributor.advisorVitrià i Marca, Jordi-
dc.contributor.authorJulià Carrillo, Oriol-
dc.date.accessioned2017-04-06T09:14:08Z-
dc.date.available2017-04-06T09:14:08Z-
dc.date.issued2016-06-26-
dc.identifier.urihttp://hdl.handle.net/2445/109443-
dc.descriptionTreballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2016, Director: Jordi Vitrià i Marcaca
dc.description.abstractLatent Dirichlet Allocation (LDA) are a suite of algorithms that are often used for topic modeling. We study the statistical model behind LDA and review how tensor methods can be used for learning LDA, as well as implement a variation of an already existing method. Next, we present an innovative algorithm for temporal topic modeling and provide a new dataset for learning topic models over time. Last, we create a visualization for the word-topic probabilities.ca
dc.format.extent59 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Oriol Julià Carrillo, 2016-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es-
dc.subject.classificationTractament del llenguatge natural (Informàtica)-
dc.subject.classificationTreballs de fi de grau-
dc.subject.classificationAprenentatge automàticca
dc.subject.classificationProbabilitatsca
dc.subject.classificationAlgorismes computacionalsca
dc.subject.otherNatural language processing (Computer science)-
dc.subject.otherBachelor's thesis-
dc.subject.otherMachine learningeng
dc.subject.otherProbabilitieseng
dc.subject.otherComputer algorithmseng
dc.titleA tensor based approach for temporal topic modelingca
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Matemàtiques

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