1. A training module implemented on one or more computers comprised of one or more processors and storage, for training a tagging model, the training module comprising: logic that receives and stores an explicitly-labeled training set in the storage, the explicitly-labeled training set including explicit labels that have been manually selected; logic that receives and stores an implicitly-labeled training set in the storage, the implicitly-labeled training set including implicit labels that have been generated by a labeling system; and logic performed by the one or more processors that trains a tagging model based on the explicitly-labeled training set and the implicitly-labeled training set, and that stores the tagging model in the storage, wherein the logic that trains also maximizes a training objective, wherein the training objective is a function of implicit label information, query information, and state variable information.
6. The training module of claim 1 , wherein the training objective applies a representation of conditional probability that includes, in part, a soft evidence feature function, wherein the soft evidence feature function expresses an influence of the implicit labels in training the tagging model.