Multinomial Mixture Modelling for Bilingual Text Classification

Jorge Civera; Alfons Juan

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Paper

Multinomial Mixture Modelling for Bilingual Text Classification

In 6th International Workshop on Pattern Recognition in Information Systems - Volume 0ICEIS, 93-103, 2006 , Paphos, Cyprus

Authors: Jorge Civera and Alfons Juan

Affiliation: Universitat Politècnica de València, Spain

Abstract: Mixture modelling of class-conditional densities is a standard pattern classification technique. In text classification, the use of class-conditional multinomial mixtures can be seen as a generalisation of the Naive Bayes text classifier relaxing its (class-conditional feature) independence assumption. In this paper, we describe and compare several extensions of the class-conditional multinomial mixture-based text classifier for bilingual texts.

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Paper citation in several formats:

Civera, J. and Juan, A. (2006). Multinomial Mixture Modelling for Bilingual Text Classification. In 6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS; ISBN 978-972-8865-55-9, SciTePress, pages 93-103. DOI: 10.5220/0002471900930103

@conference{pris06,
author={Jorge Civera. and Alfons Juan.},
title={Multinomial Mixture Modelling for Bilingual Text Classification},
booktitle={6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS},
year={2006},
pages={93-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002471900930103},
isbn={978-972-8865-55-9},
}

TY - CONF

JO - 6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS
TI - Multinomial Mixture Modelling for Bilingual Text Classification
SN - 978-972-8865-55-9
AU - Civera, J.
AU - Juan, A.
PY - 2006
SP - 93
EP - 103
DO - 10.5220/0002471900930103
PB - SciTePress