7. In a language model defining a probability for sequences of words, the language model invoked by a production application responsive to an end user for performing statistical language recognition services, a method of assigning words to classes comprising: defining a language model for predicting a likelihood of sequences of words received from the production application, the language model having a classing function, the classing function for assigning words to classes, the word classes grouping words for receiving similar treatment as other words in the class; identifying a clustering, the clustering defining the number of words in sequence to which a prediction applies, in which an n-gram cluster defines a probability that, for an n−1 sequence of words, the successive nth word will be found; identifying a language context corresponding to the usage of the language as received by the production application; defining the classing function, the classing function for scanning a learning set and identifying the word classes by: employing a word based classing approach; backing off, if the word based approach indicates a null probability; and employing a class based approach; further comprising: determining seen and unseen clusters, the unseen clusters having a previously unoccurring sequence of words; employing the word based classification if the cluster has a previous occurrence, identifying a discount parameter, the discount parameter reducing a count of word occurrences of a particular cluster in favor of a class count of words of the cluster; and backing off using the discount parameter and employing a class based approach if the cluster is unseen, unseen clusters based on occurrence of any of the words in the cluster, the unseen words has occurred the discount parameter reducing a count of word occurrences of a particular cluster in favor of a class count of words of the cluster; the discount parameter defining an absolute discounting model, further comprising: identifying a discount parameter indicative; of a reduction of a word count of words in a cluster; determining if the cluster is to be pruned or retained in the corpus; count of the observed word based count of the cluster to compute a count of the observed word based count of the cluster to compute a discounted count; or defining the discount count of the cluster as zero if the cluster is pruned; applying the classing function to the learning set to generate the word classes, the word classes indicative of words statistically likely to be employed based on predetermined sequences of words in the learning set; and optimizing the classing function by selecting the word classes based on an objective of the production application, optimizing further including: analyzing word counts and class counts of the learning set; and analyzing word frequency within an assigned class; the objective of the production application defined by the identified language context.
8. The method of claim 7 wherein the clustering being at least 3.