Title:	Score normalization methods applied to topic identification
Other Titles:	Metody normalizace skóre použité pro identifikaci tématu
Authors:	Skorkovská, Lucie Zajíc, Zbyněk
Citation:	SKORKOVSKÁ, 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.
Issue Date:	2014
Publisher:	Springer
Document type:	článek article
URI:	http://www.kky.zcu.cz/cs/publications/LucieSkorkovska_2014_ScoreNormalization http://hdl.handle.net/11025/17046
ISBN:	978-3-319-10815-5
Keywords:	identifikace tématu;multi-label klasifikace textu;naivní bayesovská klasifikace;normalizace skóre
Keywords in different language:	topic identification;multi-label text classification;naive bayes classification;score normalization
Abstract in different language:	Multi-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.
Rights:	© Lucie Skorkovská - Zbyněk Zajíc
Appears in Collections:	Články / Articles (NTIS)

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