Systematic knowledge acquisition for question analysis

For the task of turning a natural language question into an explicit intermediate representation of the complexity in question answering systems, all published works so far use rulebased approach to the best of our knowledge. We believe it is because of the complexity of the representation and the variety of question types and also there are no publicly available corpus of a decent size. In these rule-based approaches, the process of creating rules is not discussed. It is clear that manually creating the rules in an ad-hoc manner is very expensive and error-prone. In this paper, we focus on the process of creating those rules manually, in a way that consistency between rules is maintained and the effort to create a new rule is independent of the size of the current rule set. Experimental results are promising where our system achieves better performance and requires much less time and cognitive load compared to previous work.