1. A computer-implemented classification method, comprising: estimating a set of parameters for each of a plurality of n-gram language models, each n-gram language model being associated with a different category of subject matter, the sets of parameters being estimated utilizing training data that includes main data and held-out data, the sets of parameters including relative frequencies that are estimated using the main data, the relative frequencies being estimated utilizing a context sensitive conditional maximum likelihood (CML) weight, the context sensitive CML weight being set to a maximum value that is greater than zero at each iteration and then lowered separately for each context, the relative frequencies being iteratively re-estimated utilizing a ratio of a maximum likelihood count to a CML count; utilizing a computer processor that is a functional component of a computer to produce the sets of parameters for at least two of the n-gram language models jointly in relation to one another through an application of a rational function growth transform, a number of iterations of the rational function growth transform being tuned using the held-out data, the maximum value of the CML weight also being tuned using the held-out data; and utilizing the sets of parameters of the plurality of n-gram language models that are produced at least partially jointly in relation to one another as a basis for supporting a determination as to which of the categories of subject matter is best correlated to a given natural language input, wherein the category of subject matter that is best correlated to the given natural language input is information other than a textual representation of the given natural language input itself.
1. A computer-implemented classification method, comprising: estimating a set of parameters for each of a plurality of n-gram language models, each n-gram language model being associated with a different category of subject matter, the sets of parameters being estimated utilizing training data that includes main data and held-out data, the sets of parameters including relative frequencies that are estimated using the main data, the relative frequencies being estimated utilizing a context sensitive conditional maximum likelihood (CML) weight, the context sensitive CML weight being set to a maximum value that is greater than zero at each iteration and then lowered separately for each context, the relative frequencies being iteratively re-estimated utilizing a ratio of a maximum likelihood count to a CML count; utilizing a computer processor that is a functional component of a computer to produce the sets of parameters for at least two of the n-gram language models jointly in relation to one another through an application of a rational function growth transform, a number of iterations of the rational function growth transform being tuned using the held-out data, the maximum value of the CML weight also being tuned using the held-out data; and utilizing the sets of parameters of the plurality of n-gram language models that are produced at least partially jointly in relation to one another as a basis for supporting a determination as to which of the categories of subject matter is best correlated to a given natural language input, wherein the category of subject matter that is best correlated to the given natural language input is information other than a textual representation of the given natural language input itself. 2. The method of claim 1 , wherein the category of subject matter that is best correlated to the given natural language input includes a plurality of categories, the plurality of categories being ranked and displayed to a user through a user interface such that the user can select one of the plurality of categories, and wherein producing the sets for at least two of the n-gram language models jointly in relation to one another comprises producing all sets of parameters of the plurality of n-gram language models jointly in relation to one another.