Making good choices of non-redundant n-gramwords

A new complete proposal to solve the problem of automatically selecting good and non redundant n-gram words as attributes for textual data is proposed. Generally, the use of n-gram words is required to improve the subjective interpretability of a text mining task, with n ges 2. In these cases, the n-gram words are statistically generated and selected, which always implies in redundancy. The proposed method eliminates only the redundancies. This can be observed by the results of classifiers over the original and the non redundant data sets, because, there is not a decrease in the categorization effectiveness. Additionally, the method is useful for any kind of machine learning process applied to a text mining task.