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

AI in Healthcare


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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.

Please cite our work using the BibTeX below.

  author    = {Anusri Pampari and
               Preethi Raghavan and
               Jennifer J. Liang and
               Jian Peng},
  title     = {emrQA: {A} Large Corpus for Question Answering on Electronic Medical
  journal   = {CoRR},
  volume    = {abs/1809.00732},
  year      = {2018},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1809.00732},
  timestamp = {Tue, 04 Jun 2019 21:08:34 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

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