The learning vector quantization algorithm applied to automatic text classification tasks.
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.
Martín-Valdivia
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MT
Department of Computing, University of Jaén, Campus Las Lagunillas s/n, Edificio A3, Jaén E-23071, Spain. maite@ujaen.es
Ureña-López
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LA
García-Vega
M
M
D000465:Algorithms; D006801:Humans; D007858:Learning; D009323:Natural Language Processing; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*