In this paper, we show that we can obtain a good baseline performance for Question Answering (QA) by using only 4 simple features. Using these features, we contrast two approaches used for a Maximum Entropy based QA system. We view the QA problem as a classification problem and as a reranking problem. Our results indicate that the QA system viewed as a reranker clearly outperforms the QA system used as a classifier. Both systems are trained using the same data.
Early work on question answering REF found that treating the task as a maximum entropy re-ranking problem outperformed using the same system as a multiclass classifier.
Statistical QA - Classifier Vs. Re-Ranker: What's The Difference?
Workshop On Multilingual Summarization And Question Answering
Computer Science