In Question Answering, Two Heads Are Better Than One 
 Motivated by the success of ensemble methods in machine learning and other areas of natural language processing , we developed a multi-strategy and multi-source approach to question answering which is based on combining the results from different answering agents searching for answers in multiple corpora . The answering agents adopt fundamentally different strategies, one utilizing primarily knowledge-based mechanisms and the other adopting statistical techniques . We present our multi-level answer resolution algorithm that combines results from the answering agents at the question, passage, and/or answer levels . Experiments evaluating the effectiveness of our answer resolution algorithm show a 35.0% relative improvement over our baseline system in the number of questions correctly answered , and a 32.8% improvement according to the average precision metric . 