Finding what matters in questions

Abstract

In natural language question answering (QA) systems, questions often contain terms and phrases that are critically important for retrieving or finding answers from documents. We present a learnable system that can extract and rank these terms and phrases (dubbed mandatory matching phrases or MMPs), and demonstrate their utility in a QA system on Internet discussion forum data sets. The system relies on deep syntactic and semantic analysis of questions only and is independent of relevant documents. Our proposed model can predict MMPs with high accuracy. When used in a QA system features derived from the MMP model improve performance significantly over a state-of-The-Art baseline. The final QA system was the best performing system in the DARPA BOLT-IR evaluation.