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  • Title: The learning vector quantization algorithm applied to automatic text classification tasks.
    Author: Martín-Valdivia MT, Ureña-López LA, García-Vega M.
    Journal: Neural Netw; 2007 Aug; 20(6):748-56. PubMed ID: 17368839.
    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.
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