How does DeepQAs approach compare to purely knowledge-based approaches?
Classic knowledge-based AI approaches to Question Answering (QA) try to logically prove an answer is correct from a logical encoding of the question and all the domain knowledge required to answer it. Such approaches are stymied by two problems: the prohibitive time and manual effort required to acquire massive volumes of knowledge and formally encode it as logical formulas accessible to computer algorithms, and the difficulty of understanding natural language questions well enough to exploit such formal encodings if available. Consequently they tend to falter in terms of breadth, but when they succeed they are very precise.
