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dc.contributor.authorSkorkovská, Lucie
dc.date.accessioned2015-12-14T07:44:08Z
dc.date.available2015-12-14T07:44:08Z
dc.date.issued2013
dc.identifier.citationSKORKOVSKÁ, Lucie. Dynamic threshold selection method for multi-label newspaper topic identification. In: International conference on image analysis and recognition. Berlin: Springer, 2013, p. 209-216. (Lecture notes in computer science; 8082). ISBN 978-3-642-40584-6.en
dc.identifier.isbn978-3-642-40584-6
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/SkorkovskaL_2013_DynamicThreshold
dc.identifier.urihttp://hdl.handle.net/11025/16982
dc.format8 s.
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture notes in computer science; 8082en
dc.rights© Lucie Skorkovskács
dc.subjectidentifikace tématucs
dc.subjectmulti-label klasifikace textucs
dc.subjectjazykové modelovánícs
dc.subjectnaivní bayesovská klasifikacecs
dc.titleDynamic threshold selection method for multi-label newspaper topic identificationen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedNowadays, the multi-label classification is increasingly required in modern categorization systems. It is especially essential in the task of newspaper article topics identification. This paper presents a method based on general topic model normalisation for finding a threshold defining the boundary between the "correct" and the "incorrect" topics of a newspaper article. The proposed method is used to improve the topic identification algorithm which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module uses the Naive Bayes classifier for the multiclass and multi-label classification problem and assigns to each article the topics from a defined quite extensive topic hierarchy - it contains about 450 topics and topic categories. The results of the experiments with the improved topic identification algorithm are presented in this paper.en
dc.subject.translatedtopic identificationen
dc.subject.translatedmulti-label text classificationen
dc.subject.translatedlanguage modellingen
dc.subject.translatednaive bayes classificationen
dc.identifier.doi10.1007/978-3-642-40585-3_27
dc.type.statusPeer-revieweden
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