10. A method in a computer system for generating models that each model text in one of a number of languages, the method comprising the steps of: receiving a plurality of sequences of data values, each sequence of data values representing text in an identified language, each data value being one of a number of possible data values; for each sequence of data values: using a reductive mapping to map the data values of the sequence from the number of possible data values to target values among a smaller number of possible target values to produce a sequence of derived target values corresponding to the sequence of data values; for each n-gram of possible target values: counting the number of occurrences of the n-gram in the sequence of derived target values; and combining the counted number of n-grams of target values into a model for the language identified for the sequence of data values, the model reflecting the relative frequency of each n-gram of possible target values in all of the sequences of derived target values for which the language identified for the sequence of values is identified.
10. A method in a computer system for generating models that each model text in one of a number of languages, the method comprising the steps of: receiving a plurality of sequences of data values, each sequence of data values representing text in an identified language, each data value being one of a number of possible data values; for each sequence of data values: using a reductive mapping to map the data values of the sequence from the number of possible data values to target values among a smaller number of possible target values to produce a sequence of derived target values corresponding to the sequence of data values; for each n-gram of possible target values: counting the number of occurrences of the n-gram in the sequence of derived target values; and combining the counted number of n-grams of target values into a model for the language identified for the sequence of data values, the model reflecting the relative frequency of each n-gram of possible target values in all of the sequences of derived target values for which the language identified for the sequence of values is identified. 15. The method of claim 10 wherein the n-grams are bigrams comprised of two target values adjacent in the sequence of target values.