The learning vector quantization algorithm applied to automatic text classification tasks
✍ Scribed by M.T. Martín-Valdivia; L.A. Ureña-López; M. García-Vega
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 998 KB
- Volume
- 20
- Category
- Article
- ISSN
- 0893-6080
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✦ Synopsis
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.