Finding What Matters in Questions

Xiaoqiang Luo, Hema Raghavan, Vittorio Castelli, Sameer Maskey and Radu Florian

In natural language question answering (QA) systems, questions often contain terms and phrases that are critically important for retrieving or finding anwers 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.