7. The apparatus of claim 5 , wherein the plurality of features comprises one or more features from the set consisting of: a number of times a target concept is referenced in the corpus of knowledge; a feature indicating a topicality or centrality of a topic; syntactic clues; intersentential discourse clues; discourse structure attributes; a frequency of a predicate/argument structure in the corpus of knowledge; a position in the corpus of knowledge of an initial mention of a concept; whether a definition of a term is contained within the corpus of knowledge; a degree to which a term is used in a sentence in an unexpected context compared to a typical context within the corpus of knowledge.
5. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: receive an input question; generate one or more candidate answers from a corpus of knowledge using a pipeline of software engines, wherein the pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge; generate a question difficulty score based on the plurality of features using a machine learning model, wherein the machine learning model maps features to assigned weights for scaling the difficulty score; store the question difficulty score in association with the input question to form a stored set of questions; receive a question policy, wherein the question policy comprises a target difficulty level and a threshold; and select a set of questions from the stored set of questions, wherein the set of questions have a difficulty score within the threshold of the target difficulty level.