1. A computer implemented system for automatic document classification, the system comprising: an extraction module configured to extract structural, syntactical and semantic information from a document and normalize the extracted information; a machine learning module configured to generate 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 a classification module configured to select a non-classified document from a document collection, and via the extraction module extract normalized structural, syntactical and semantic information from the selected document, and generate via the machine learning module a model representation of the selected document based on feature vectors, and match the model representation of the selected document against the machine learning model representation to generate 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.
4. The system of claim 1 , 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).