Automatic document classification (Taxonomy) is the use of software to sort documents into various pre-defined categories. A similar task is automatic document clustering, in which there are no pre-defined categories, so the software must create the categories by itself. If you wish to use the xAIgent to generate feature vectors, we suggest the following approach:
Apply the xAIgent to all of the documents in your sample collection.
Take the union of all of the extracted keyphrases as the feature set.
For each document and each feature, let the value of the feature be the number of times that the given phrase occurs in the given document (regardless of whether the xAIgent extracted it from the given document).
Apply your favourite machine learning algorithm (e.g., decision tree induction, neural network, genetic algorithm, etc.) to the resulting feature vectors.
If you want to learn more about automatic document classification and clustering, there is a hypertext Bibliography on Machine Learning Applied to Text
http://www.researchgate.net/publication. xAIgent can be used to generate features for use in feature vectors for machine learning algorithms.