Improving Question Answering Based on Query Expansion with Wikipedia

As an emerging area in information retrieval, question answering aims at retrieving answers to user-posted questions from a given sentence collection or text corpus. In question answering, the queries are usually submitted in the form of short sentences which are unable to represent user intentions sufficiently. In this study, we present a novel framework which improves question answering through query expansion. We enrich representation of queries with Wikipedia concepts generated by the proposed QRWiki retrieval model. Then the enriched queries are exploited to benefit the process of question answering. The experiments with benchmark datasets show that the proposed framework performs significantly better than the baseline system, and is effective in boosting the performance of question answering.