The learning vector quantization algorithm applied to automatic text classification tasks

Authors:
M. T. Martín-Valdivia;L. A. Ureña-López;M. García-Vega
Affiliations:
Department of Computing, University of Jaén, Campus Las Lagunillas s/n, Edificio A3, Jaén, E-23071, Spain;Department of Computing, University of Jaén, Campus Las Lagunillas s/n, Edificio A3, Jaén, E-23071, Spain;Department of Computing, University of Jaén, Campus Las Lagunillas s/n, Edificio A3, Jaén, E-23071, Spain
Venue:
Neural Networks
Year:
2007

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Abstract

Automatic text classification is an important task for many natural language processing applications. This paper presents a neural approach to develop a text classifier based on the Learning Vector Quantization (LVQ) algorithm. The LVQ model is a classification method that uses a competitive supervised learning algorithm. The proposed method has been applied to two specific tasks: text categorization and word sense disambiguation. Experiments were carried out using the REUTERS-21578 text collection (for text categorization) and the SENSEVAL-3 corpus (for word sense disambiguation). The results obtained are very promising and show that our neural approach based on the LVQ algorithm is an alternative to other classification systems.