1. A method of creating a statistical classification model for use with a natural language understanding system, the method comprising: processing training data using an existing statistical classification model; selecting sentences of the training data correctly classified into a selected class of the existing statistical classification model; assigning each selected sentence of the training data to a fringe group or a core group according to confidence score; updating the training data by associating the fringe group with a fringe subclass of the selected class and the core group with a core subclass of the selected class; building a new statistical classification model from the updated training data; and outputting the new statistical classification model.