Abstract: Machines, systems and methods for maintaining a representative data set in a document classification system, the method comprising: including an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of the representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; updating the RDS by adding or removing representative data from the RDS based on feedback received about accuracy of classification of one or more documents by the classification system; and retraining the KB, wherein the retraining is performed based on occurrence of one or more events.

Abstract: Machines, systems and methods for classifying documents, the method comprising: classifying a document from among a plurality of documents in a first class, in response to applying statistical analysis to data associated with the document; classifying the document in a second class, in response to determining that a rule from among a plurality of rules applies to the document, wherein a proposed rule is added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable does not diminish accuracy of overall classification for the plurality of documents.

Abstract: Machines, systems and methods for maintaining a representative data set in a document classification system, the method comprising: including an initial set of seed representative data in a representative data set (RDS) implemented for a knowledge base (KB), wherein the KB is trained to classify documents provided to a document classification system based on analysis of the representative documents included in the RDS and a set of rules, wherein the seed representative data includes a balanced number of representative data across a plurality of classes; updating the RDS by adding or removing representative data from the RDS based on feedback received about accuracy of classification of one or more documents by the classification system; and retraining the KB, wherein the retraining is performed based on occurrence of one or more events.

Abstract: Machines, systems and methods for classifying documents, the method comprising: classifying a document from among a plurality of documents in a first class, in response to applying statistical analysis to data associated with the document; classifying the document in a second class, in response to determining that a rule from among a plurality of rules applies to the document, wherein a proposed rule is added to the plurality of rules, in response to determining that application of the proposed rule to one or more of the plurality of documents to which the rule is applicable does not diminish accuracy of overall classification for the plurality of documents.

Abstract: A system, method and computer program product for scalable, rule-based processing, including an automaton builder for constructing automatons corresponding to trigger rules and word lists that are employed by the trigger rules, and a rule evaluator for evaluating any of the trigger rules with respect to an input document by selecting any of the automatons to evaluate a given one of the trigger rules, parsing the input document using the selected automatons, determining whether conditions of the given trigger rule are met, and identifying any actions that are associated with the given trigger rule.

Abstract: A method, system and computer program product for building an expression, including utilizing any formal grammar of a context-free language, displaying an expression on a computer display via a graphical user interface, replacing at least one non-terminal display object within the displayed expression with any of at least one non-terminal display object and at least one terminal display object, and repeating the replacing step a plurality of times for a plurality of non-terminal display objects until no non-terminal display objects remain in the displayed expression, wherein the non-terminal display objects correspond to non-terminal elements within the grammar, and wherein the terminal display objects correspond to terminal elements within the grammar.

Abstract: A system, method and computer program product for scalable, rule-based processing, including an automaton builder for constructing automatons corresponding to trigger rules and word lists that are employed by the trigger rules, and a rule evaluator for evaluating any of the trigger rules with respect to an input document by selecting any of the automatons to evaluate a given one of the trigger rules, parsing the input document using the selected automatons, determining whether conditions of the given trigger rule are met, and identifying any actions that are associated with the given trigger rule.