1. A computer-implemented method, comprising: estimating a set of parameters for a plurality of n-gram language models that each correspond to a different class, wherein each class corresponds to a different category of subject matter, and wherein estimating comprises: setting initial values for each n-gram language model's sets of parameters; and adjusting each n-gram language model's sets of parameters jointly in relation to one another to increase a conditional likelihood of a class corresponding to a category of subject matter given a word string; and utilizing the n-gram language models' sets of parameters as a basis for supporting a determination as to which of the plurality of classes represents the category of subject matter that is best correlated to a given natural language input.