Modeling Web Knowledge for Answering Event-based Questions

For the TREC-style questions, the query terms we get from the original questions are either too brief or often do not contain much relevant information in the corpus. It will be very difficult to find an exact answer in a large corpus because of the surface string mismatch. In order to solve this problem, we present a question answering system QUALIFIER, which employs a novel approach to structurally model the external knowledge from the Web and other resource for Event-based question answering. The results obtained on TREC-11 QA corpus demonstrate the effectiveness of our approach.