Score Normalization Methods Applied to Topic Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F14%3A43922926" target="_blank" >RIV/49777513:23520/14:43922926 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-10816-2_17" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-10816-2_17</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-10816-2_17" target="_blank" >10.1007/978-3-319-10816-2_17</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Score Normalization Methods Applied to Topic Identification

Popis výsledku v původním jazyce

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 ano

Název v anglickém jazyce

Score Normalization Methods Applied to Topic Identification

Popis výsledku anglicky

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 ano

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/LM2010013" target="_blank" >LM2010013: LINDAT-CLARIN: Institut pro analýzu, zpracování a distribuci lingvistických dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Text, Speech, and Dialogue, 17th International Conference, TSD 2014, Brno, Czech Republic, September 8-12, 2014. Proceedings

ISBN

978-3-319-10815-5

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

8

Strana od-do

133-140

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Brno, Czech Republic

Datum konání akce

8. 9. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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