Speeding-up text categorization in a grid computing environment

The amount of texts available in digital form has dramatically increased, giving rise to the need of fast text classifiers. The tasks involved can be parallelized and distributed in a grid environment. This paper reports a study conducted on Reuters-21578 corpus, using a SVM learning machine. The task of text categorization is distributed in several platforms. The results achieved are very promising for speeding-up text categorization tasks and are valid independently of the learning machine.