An unsupervised method to improve Spanish stemmer

We evaluate the effectiveness of using our edit distances algorithm to improving an unsupervised language-independent stemming method. The main idea is to create morphological families through the automatic words grouping using our distance. Based on that grouping, we make a stemming process. The capacity of the edit distance algorithm in the task of words clustering and the ability of our method to generate the correct stem for Spanish was evaluated. A good result (98% precision) for the morphological families' creation and also a remarkable 99.85% of correct stemming was obtained.