A Sys Called Qanda

Our question answering system was built with a number of priorities in mind. First, we wanted to experiment with natural language processing (NLP) technologies such as shallow parsing, named entity tagging, and coreference chaining. We felt that the small number of terms in the questions coupled with the short length of the answers would make NLP technologies clearly beneficial, unlike previous experiments with NLP technologies on traditional IR tasks. At a more practical level, we were familiar with and interested in such technologies and thus their use would be relatively straightforward and enjoyable. Second, we wanted to use information retrieval (IR) techniques in hopes of achieving robustness and efficiency. It seemed obvious that many answers would appear in documents and passages laden with terms from the question. Finally, we wanted to experiment with different modules from different sites with differing input and output representation and implementational details. Thus, we needed a multi-process system with a flexible data format.