Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/43258
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dc.creatorLuís Sarmento
dc.creatorSérgio Nunes
dc.creatorJorge Teixeira
dc.creatorEugénio Oliveira
dc.date.accessioned2017-03-30T23:15:32Z-
dc.date.available2017-03-30T23:15:32Z-
dc.date.issued2009
dc.identifier.other57576
dc.identifier.urihttp://hdl.handle.net/10216/43258-
dc.description.abstractWe propose an unsupervised method for propagating automatically extracted fine-grained topic labels among news items to improve their topic description for subsequent text classification procedure. This method compares vector representations of news items and assigns to each news item the label of its closest neighbour with a different topic label. Results obtained show that high precision can be achieved in propagating the top ranked topic label, and that 2-gram and 3-gram feature representations optimize the precision.
dc.language.isoeng
dc.relation.ispartof2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 2
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectInteligência artificial, Ciências da computação e da informação
dc.titlePropagating fine-grained topic labels in news snippets
dc.typeArtigo em Livro de Atas de Conferência Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.1109/wi-iat.2009.338
dc.identifier.authenticusP-003-SWF
dc.subject.fosCiências exactas e naturais::Ciências da computação e da informação
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

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