Importance

Loading Importance Indicators...

Abstract

A classifier for semi-structured documents and associated method dynamically and accurately classify documents with an implicit or explicit schema by taking advantage of the term-frequency and term distribution information inherent in the document. The system uses a structured vector model that allows like terms to be grouped together and dissimilar terms to be segregated based on their frequency and distribution within the sub-vectors of the structure vector, thus achieving context sensitivity. The final decision for assigning the class of a document is based on a mathematical comparison of the similarity of the terms in the structured vector to those of the various class models. The classifier of the present invention is capable of both learning and testing. In the learning phase the classifier develops models for classes with information it develops from the composite information gleaned from numerous training documents. Specifically, it develops a structured vector model for each training document. Then, within a given class of documents it adds and then normalizes the occurrences of terms.

Loading the Abstract Image...

First Claim