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emrQA: A Large Corpus for Question Answering on Electronic Medical Records

Research paper by Anusri Pampari, Preethi Raghavan, Jennifer Liang, Jian Peng

Indexed on: 03 Sep '18Published on: 03 Sep '18Published in: arXiv - Computer Science - Computation and Language



Abstract

We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million question-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.