Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multistrategy 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.
REF presented a multi-level answer resolution algorithm to merge results from the answering agents at the question, passage, and answer levels.
In Question Answering, Two Heads Are Better Than One
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science