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dc.contributor.authorSkorkovská, Lucie
dc.contributor.authorZajíc, Zbyněk
dc.date.accessioned2015-12-17T10:53:38Z
dc.date.available2015-12-17T10:53:38Z
dc.date.issued2014
dc.identifier.citationSKORKOVSKÁ, Lucie; ZAJÍC, Zbyněk. Score normalization methods applied to topic identification. In: Text, speech and dialogue. Berlin: Springer, 2014, p. 133-140. (Lecture notes in computer science; 8655). ISBN 978-3-319-10815-5.en
dc.identifier.isbn978-3-319-10815-5
dc.identifier.urihttp://www.kky.zcu.cz/cs/publications/LucieSkorkovska_2014_ScoreNormalization
dc.identifier.urihttp://hdl.handle.net/11025/17046
dc.format8 s.
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture notes in computer science; 8655en
dc.rights© Lucie Skorkovská - Zbyněk Zajíccs
dc.subjectidentifikace tématucs
dc.subjectmulti-label klasifikace textucs
dc.subjectnaivní bayesovská klasifikacecs
dc.subjectnormalizace skórecs
dc.titleScore normalization methods applied to topic identificationen
dc.title.alternativeMetody normalizace skóre použité pro identifikaci tématucs
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedMulti-label classification plays the key role in modern categorization systems. Its goal is to find a set of labels belonging to each data item. In the multi-label document classification unlike in the multi-class classification, where only the best topic is chosen, the classifier must decide if a document does or does not belong to each topic from the predefined topic set. We are using the generative classifier to tackle this task, but the problem with this approach is that the threshold for the positive classification must be set. This threshold can vary for each document depending on the content of the document (words used, length of the document, ...). In this paper we use the Unconstrained Cohort Normalization, primary proposed for speaker identification/verification task, for robustly finding the threshold defining the boundary between the correct and the incorrect topics of a document. In our former experiments we have proposed a method for finding this threshold inspired by another normalization technique called World Model score normalization. Comparison of these normalization methods has shown that better results can be achieved from the Unconstrained Cohort Normalization.en
dc.subject.translatedtopic identificationen
dc.subject.translatedmulti-label text classificationen
dc.subject.translatednaive bayes classificationen
dc.subject.translatedscore normalizationen
dc.identifier.doi10.1007/978-3-319-10816-2_17
dc.type.statusPeer-revieweden
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