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Automatic question generation can play a vital role in educational applications such as intelligent tutoring systems. Prior work in question generation relies primarily on one view of the sentence provided by a parser of a given type, such as phrase structure trees or predicate argument structure. In contrast, we explore using multiple views from different parsers to create a tree structure which represents items of interest for question generation. This approach resulted in a 17% reduction in the error rate compared with our prior work, which achieved a 44% reduction in the error rate compared to state-of-the-art question generation systems. Additionally, the work presented in this paper generates with greater question variety than our previous work, and creates 21% more semantically-oriented versus factoid questions.