Abstract: A system and method for classifying unstructured text documents, without the need for pre-classified training examples. In general, the system and method provides for blending statistical, syntactic and semantic considerations to learn classifiers from an organization's unclassified internal and external unstructured text documents, as well as unclassified documents available via the Internet. In one form, for each class in a taxonomy the class name is expanded into semantically related words and phrases to build approximate classifiers. Each approximate classifier will almost certainly be erroneous but it can be used to identify an approximately correct set of documents. The process is recursive; e.g. the approximate classifier with the strongest evidence, is fed back into the system until a stale set of the strongest terms for each classifier has been selected.

Abstract: A method and system for hierarchically classifying text documents, using scoring and ranking. In particular, the present invention provides a system and method for classifying text documents, where terms in the document are associated with a class drawn from a taxonomy and used to calculate a score for each class. In one form, terms are captured for each class and adjustments made to compute a score to classify a document into a class. Using the scores, the top classes in a document are computed. Advantageously, the method and system can explain the classification, including why a class was not considered.

Abstract: A system and method for classifying unstructured text documents, without the need for pre-classified training examples. In general, the system and method provides for blending statistical, syntactic and semantic considerations to learn classifiers from an organization's unclassified internal and external unstructured text documents, as well as unclassified documents available via the Internet. In one form, for each class in a taxonomy the class name is expanded into semantically related words and phrases to build approximate classifiers. Each approximate classifier will almost certainly be erroneous but it can be used to identify an approximately correct set of documents. The process is recursive; e.g. the approximate classifier with the strongest evidence, is fed back into the system until a stale set of the strongest terms for each classifier has been selected.