Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/44319
DC FieldValueLanguage
dc.contributor.authorRodrigues, Filipe-
dc.contributor.authorLourenco, Mariana-
dc.contributor.authorRibeiro, Bernardete-
dc.contributor.authorPereira, Francisco-
dc.date.accessioned2017-11-08T20:23:53Z-
dc.date.available2017-11-08T20:23:53Z-
dc.date.issued2017-
dc.identifier10.1109/TPAMI.2017.2648786-
dc.identifier.urihttp://hdl.handle.net/10316/44319-
dc.description.abstractThe growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.por
dc.language.isoengpor
dc.publisherIEEEpor
dc.rightsopenAccesspor
dc.titleLearning Supervised Topic Models for Classification and Regression from Crowdspor
dc.typearticle-
degois.publication.firstPage1por
degois.publication.lastPage1por
degois.publication.titleIEEE Transactions on Pattern Analysis and Machine Intelligencepor
dc.peerreviewedyespor
dc.identifier.doi10.1109/TPAMI.2017.2648786por
uc.controloAutoridadeSim-
item.fulltextCom Texto completo-
item.languageiso639-1en-
item.grantfulltextopen-
crisitem.author.deptFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.author.parentdeptUniversidade de Coimbra-
crisitem.author.researchunitCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA-
crisitem.author.orcid0000-0002-9770-7672-
Appears in Collections:FCTUC Eng.Informática - Artigos em Revistas Internacionais
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