1. A method implemented by a question answer system that includes a memory and a processor, the method comprising: training an unreasonable answer filter using a set of training questions, a set of training answers corresponding to the set of training questions, and a set of unreasonable answers corresponding to the set of training questions; computing, by the unreasonable answer filter, one or more unreasonable answer probabilities of one or more ranked answers in a set of ranked answers, wherein each of the one or more unreasonable answer probabilities indicate a likelihood that its corresponding ranked answer is an incorrect answer to a question; determining, by the unreasonable answer filter, that at least one of the one or more unreasonable answer probabilities reaches a threshold, indicating that their one or more corresponding ranked answers are one or more unreasonable answers to the question; removing the one or more unreasonable answers from the set of ranked answers, resulting in a modified set of ranked answers; and providing the modified set of ranked answers to a user.
3. The method of claim 1 further comprising: generating, based upon the training, a feature set graph that includes both a correct/incorrect hyperplane and an unreasonable answer hyperplane.