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.
@article{arxiv.1809.00732,
title = {emrQA: A Large Corpus for Question Answering on Electronic Medical Records},
author = {Anusri Pampari and Preethi Raghavan and Jennifer Liang and Jian Peng},
journal= {arXiv preprint arXiv:1809.00732},
year = {2018}
}
Comments
Accepted at Conference on Empirical Methods in Natural Language Processing (EMNLP) 2018