9. A computer program product for automatic document classification and including one or more computer readable instructions embedded on a tangible, non-transitory computer readable medium and configured to cause one or more computer processors to perform the steps of: extracting with an extraction module structural, syntactical and semantic information from a document and normalizing with the extraction module the extracted information; generating with a machine learning module a model representation for automatic document classification based on feature vectors built from the normalized and extracted semantic information for supervised and unsupervised clustering and machine learning; and selecting with a classification module a non-classified document from a document collection, and extracting via the extraction module normalized structural, syntactical and semantic information from the selected document, and generating via the machine learning module a model representation of the selected document based on feature vectors, and matching with the classification module the model representation of the selected document against the machine learning model representation and generating with the classification module a document category, and classification for display to a user, wherein the extracted information includes named entities, properties of entities, noun-phrases, facts, events, and concepts.
9. A computer program product for automatic document classification and including one or more computer readable instructions embedded on a tangible, non-transitory computer readable medium and configured to cause one or more computer processors to perform the steps of: extracting with an extraction module structural, syntactical and semantic information from a document and normalizing with the extraction module the extracted information; generating with a machine learning module a model representation for automatic document classification based on feature vectors built from the normalized and extracted semantic information for supervised and unsupervised clustering and machine learning; and selecting with a classification module a non-classified document from a document collection, and extracting via the extraction module normalized structural, syntactical and semantic information from the selected document, and generating via the machine learning module a model representation of the selected document based on feature vectors, and matching with the classification module the model representation of the selected document against the machine learning model representation and generating with the classification module a document category, and classification for display to a user, wherein the extracted information includes named entities, properties of entities, noun-phrases, facts, events, and concepts. 12. The computer program product of claim 9 , wherein the model representation of the document is a term frequency-inverse document frequency (TF-IDF) document representation of the extracted information, and the clustering and machine learning includes a classifier based on decision trees, support vector machines (SVM), naïve-bayes classifiers, k-nearest neighbors, rules-based classification, Linear discriminant analysis (LDA), Maximum Entropy Markov Model (MEMM), scatter-gather clustering, and hierarchical agglomerate clustering (HAC).