11. The non-transitory computer-readable storage of claim 8 , wherein selecting the particular target language translation based at least in part on the expectation maximum comprises selecting the particular target language translation based at least in part on a number of times in a particular set of target language documents that the particular target language translation translates into the particular source language feature under circumstances in which documents in the particular set of target language documents belong to the particular class.
8. A non-transitory computer-readable storage that stores instructions which, when executed by one or more processors, cause the one of more processors to perform the steps of: training a source language text classifier based on labeled training data that is composed in the source language, thereby producing a source language model; translating source language features from the source language model into a target language, thereby producing a target language model; and applying a target language classifier, which uses the target language model, to a particular document that is composed in the target language, thereby determining whether the particular document belongs to a particular class; wherein the source language classifier determines, based on said training, whether documents composed in the source language belong to the particular class; wherein translating the source language features comprises automatically selecting, for a particular source language feature of the source language features, a particular target language translation of a plurality of possible target language translations of the particular source language feature; and wherein selecting the particular target language translation is based at least in part on an expectation maximum; wherein selecting the particular target language translation based at least in part on the expectation maximum comprises: determining a first probability based at least in part on the particular target language translation during a first iteration; determining a second probability based at least in part on the particular target language translation during a second iteration; determining whether a difference between the first probability and the second probability is less than a specified threshold; in response to determining that the difference between the first probability and the second probability is less than the specified threshold, setting a final translation probability for the particular target language translation equal to the second probability; and selecting the particular target language translation based at least in part on the final translation probability.