13. An apparatus comprising: a storage that stores at least labeled training data that is composed in a source language; a storage that stores at least a particular document that is composed in a target language that differs from the source language; one or more processors that: (a) train a source language text classifier based on the labeled training data, thereby producing a source language model; (b) translate source language features from the source language model into a target language at least in part by selecting, for at least a particular source language feature of the source language features, and based at least in part on an expectation maximum, a particular target language translation of a plurality of possible target language translations of the particular source language feature; and (c) apply a target language classifier, which uses a target language model produced based at least in part on the selecting, to the particular document, thereby determining whether the particular document belongs to a particular class; wherein the one or more processors select the particular target language translation at least in part by: 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.
13. An apparatus comprising: a storage that stores at least labeled training data that is composed in a source language; a storage that stores at least a particular document that is composed in a target language that differs from the source language; one or more processors that: (a) train a source language text classifier based on the labeled training data, thereby producing a source language model; (b) translate source language features from the source language model into a target language at least in part by selecting, for at least a particular source language feature of the source language features, and based at least in part on an expectation maximum, a particular target language translation of a plurality of possible target language translations of the particular source language feature; and (c) apply a target language classifier, which uses a target language model produced based at least in part on the selecting, to the particular document, thereby determining whether the particular document belongs to a particular class; wherein the one or more processors select the particular target language translation at least in part by: 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. 14. The apparatus of claim 13 , wherein the one or more processors further: apply the target language classifier to a set of unlabelled documents that are composed in the target language, thereby producing a set of automatically labeled target language documents; train a new target language text classifier based on the set of automatically labeled target language documents, thereby producing a tuned target language model; and apply a new target language classifier, which uses the tuned target language model, to the particular document that is composed in the target language, thereby determining whether the particular document belongs to the particular class.