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. (c) 2007 Elsevier Ltd. All rights reserved.