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dc.contributor.advisorPeláez-Moreno, Carmen
dc.contributor.authorCordobés de la Calle, Héctor 
dc.date.accessioned2021-07-13T09:25:27Z
dc.date.available2021-07-13T09:25:27Z
dc.date.issued2014-07-14
dc.identifier.urihttp://hdl.handle.net/20.500.12761/24
dc.description.abstractTopic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag of words approach, in which the classifier uses (and is trained with) selected terms from the in put texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy.
dc.language.isoeng
dc.titleGraph-based techniques for tweet classification in Spanishen
dc.typemaster thesis
dc.type.hasVersionVoR
dc.rights.accessRightsopen access
dc.description.departmentElectronic Technology, Signal and Communications Theory, and Telematic Engineering
dc.description.institutionUniversidad Carlos III de Madrid, Spain
dc.page.total7
dc.subject.keywordTopic classification
dc.subject.keywordtext classification
dc.subject.keywordgraphs
dc.subject.keywordnatural language processing
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/1016


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