Data-driven type checking in open domain question answering

Many open domain question answering systems answer questions by first harvesting a large number of candidate answers, and then picking the most promising one from the list. One criterion for this answer selection is type checking: deciding whether the candidate answer is of the semantic type expected by the question. We define a general strategy for building redundancy-based type checkers, built around the notions of comparison set and scoring method, where the former provide a set of potential answer types and the latter are meant to capture the relation between a candidate answer and an answer type. Our focus is on scoring methods. We discuss nine such methods, provide a detailed experimental comparison and analysis of these methods, and find that the best performing scoring method performs at the same level as knowledge-intensive methods, although our experiments do not reveal a clear-cut answer on the question whether any of the scoring methods we consider should be preferred over the others.