Abstract: A system and method that records the important words lists according to a previous naive Bayes classifier for each category. If a new document provides different important words to distinguish the category from other categories, the method would then re-train the system. If the new document provides the same important words as the related important words list, the method would not re-train the system. When there are new training examples, the method must re-train the system. If the training examples come into the system one by one, the method must re-train the system again and again. Two re-training policies are taught to make the system of the present invention more effective and keep it up to date. The first policy is to regularly re-train the system everyday with all training examples. The second policy is to re-train in real time, during intervals each day.

Abstract: System and method of email document classification involving the removal of disclaimers from consideration in the classification process. The method first removes all html code and coverts the text to a standardized all lower case font. One or more matching strings are run on the content. In an alternative embodiment, disclaimers are identified and removed. One or more matching disclaimer strings are run on the document after the font and text conversion. After all disclaimer strings have been run, the document has either been unchanged, or the disclaimer sections removed per the instructions of the strings. One or more matching strings for classifying the document are then run before the process ends and the document is classified.

Abstract: The present invention generally relates to an improved system and method for providing email classification. Specifically, the present invention relates to an email classification system and method for analyzing the signature of an email for proper classification.