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