Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
404784 | Neural Networks | 2007 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
M.T. Martín-Valdivia, L.A. Ureña-López, M. García-Vega,