Abstract: A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or 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/or unsupervised clustering or 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/or 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/or classificatio

Abstract: A system, method and computer program product for validating a document classification process, including a document collection; a document classification process performed on the document collection; a random selection module configured to automatically generate a random validation set of documents from the document collection; and a document review process performed on the random validation set of documents to validate results of the document classification process. The system, method and computer program product are configured to dynamically and in real-time measure and display on a computer display device a best case estimate of a quality of the results of the document classification process based on the documents that are validated, and given a size of a total data set of the document collection.

Abstract: A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or 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/or unsupervised clustering or 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/or 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/or classificatio

Abstract: A system, method and computer program product for validating a document classification process, including a document collection; a document classification process performed on the document collection; a random selection module configured to automatically generate a random validation set of documents from the document collection; and a document review process performed on the random validation set of documents to validate results of the document classification process. The system, method and computer program product are configured to dynamically and in real-time measure and display on a computer display device a best case estimate of a quality of the results of the document classification process based on the documents that are validated, and given a size of a total data set of the document collection.

Abstract: A system, method and computer program product for authorship determination, and alias resolution, including a document collection; a Jaro-Winkler similarity module configured for performing authorship determination and alias resolution based on at least one of email addresses, user identification numbers (IDs) on social networks, names written in text, and proper names, including countries and cities in the document collection; an authorship Support Vector Machine (SVM) module configured for performing authorship determination and alias resolution based on content of documents in the document collection, including at least one of emails, and social networks information; and a Jaccard similarity module configured for performing authorship determination and alias resolution based on link networks in the document collection.

Abstract: A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or 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/or unsupervised clustering or 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/or 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/or classificatio

Abstract: A system, method and computer program product for validating a document classification process, including a document collection; a document classification process performed on the document collection; a random selection module configured to automatically generate a random validation set of documents from the document collection; and a document review process performed on the random validation set of documents to validate results of the document classification process. The system, method and computer program product are configured to dynamically and in real-time measure and display on a computer display device a best case estimate of a quality of the results of the document classification process based on the documents that are validated, and given a size of a total data set of the document collection.

Abstract: A system, method and computer program product for validating a document classification process, including a document collection; a document classification process performed on the document collection; a random selection module configured to automatically generate a random validation set of documents from the document collection; and a document review process performed on the random validation set of documents to validate results of the document classification process. The system, method and computer program product are configured to dynamically and in real-time measure and display on a computer display device a best case estimate of a quality of the results of the document classification process based on the documents that are validated, and given a size of a total data set of the document collection.

Abstract: A system, method and computer program product for authorship determination, and alias resolution, including a document collection; a Jaro-Winkler similarity module configured for performing authorship determination and alias resolution based on at least one of email addresses, user identification numbers (IDs) on social networks, names written in text, and proper names, including countries and cities in the document collection; an authorship Support Vector Machine (SVM) module configured for performing authorship determination and alias resolution based on content of documents in the document collection, including at least one of emails, and social networks information; and a Jaccard similarity module configured for performing authorship determination and alias resolution based on link networks in the document collection.

Abstract: A system, method and computer program product for automatic document classification, including an extraction module configured to extract structural, syntactical and/or 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/or unsupervised clustering or 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/or 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/or classificatio