Paper: Finding What Matters in Questions

ACL ID N13-1108
Title Finding What Matters in Questions
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

In natural language question answering (QA) systems, questions often contain terms and phrases that are critically important for re- trieving or finding answers from documents. We present a learnable system that can ex- tract and rank these terms and phrases (dubbed mandatory matching phrases or MMPs), and demonstrate their utility in a QA system on In- ternet discussion forum data sets. The system relies on deep syntactic and semantic analysis of questions only and is independent of rele- vant documents. Our proposed model can pre- dict 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 sys- tem was the best performing system in the DARPA BOLT-IR evaluation.