LVQ for text categorization using a multilingual linguistic resource
Neural learning has been used with effectiveness in natural language processing tasks. Particularly, the WidrowHoff and the KivinenWarmuth exponentiated gradient (based on neural learning rules) algorithms have been used in text categorization, improving the results obtained by the well-known Rocchio's algorithm. The high performance of competitive learning algorithms, recently applied to solve information retrieval problems, leads us to use them in the specific text categorization tasks. This paper presents a multilingual categorization system based on neural learning, using the polyglot Bible as training collection, both in Spanish and English. The method we suggest is based on using the LVQ algorithm to build a classifier that learns the training multilingual collection. We have performed experiments with the four algorithm which show that the ideas we describe are promising and are worth further investigation.
polyglot bible text categorization (tc) vector space model (vsm) learning vector quantization (lvq) multilingual information retrieval (mir)