Mining Answers from Texts and Knowledge Bases: Our Position

Bruce Porter, Ken Barker, James Fan, Paul Navratil, Dan Tecuci, Peter Yeh and Peter Clark

Recent advances in question answering from text have shown that information retrieval, natural language processing and machine learning techniques can go a long way in retrieving answers to certain types of questions from large bodies of text. Questions requiring more reasoning and inference, or those whose answers require synthesis or explanation are more difficult. Systems that reason over domin-specific knowledge bases are capable of more sophisticated behavior than answer retrieval systems, but are expensive in terms of their knowledge requirements. The problem of answering difficult questions from the knowledge exix'essed in text can be attacked from both ends: by improving answer retrieval from large corpora, and by making it possible for formal representations of knowledge contained in text to be authored more quickly and easily.

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