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