The commonly used machine learning paradigm, is concerned with learning single concepts from examples. In this framework the learner attempts to learn a single hidden function, e.g., a sense of a word in a given context, from a collection of examples. However, in many cases -- as in most natural language situations -- decisions depend on the outcomes of several different but mutually dependent classifiers. The classifiers' outcomes need to respect some constraints that could arise from the sequential nature of the data or other domain specific conditions, thus requiring a level of inference on top the predictions.
I will describe natural language research on Inference with Classifiers -- a paradigm in which we address the problem of using the outcomes of several different classifiers in making coherent inferences -- those that respect constraints on the outcome of the classifiers. The paradigm will be demonstrated using examples of sequential inference problems such as chunking, as well as language understanding problems such as discovering semantic entities and relations among them.