This research seeks to develop an integrated view - theoretical understanding, algorithms development and experimental evaluation - for learning coherent concepts. These are learning scenarios that are common in cognitive learning - where multiple learners co-exist and may learn different fuctions on the same input, but there are mutual compatibility constraints on their outcomes. Our effort will consist of developing a learning theory for this situations and of studying algorithmic ways to exploit them in natural language inferences. The theoretical study concentrates on developing a semantics for the coherency conditions and study it from a learning theory point of view. The goal is to understand in what does does learning become easier and more robust in these situations. The algorithmic study concentrates on developing ways to exploit coherency and makes use of several important problems in natural language processing as a testbed for investigating chaning of coherent classifiers and inferences that rely on the outcomes of several classifiers.