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        <title>emrQA: A Large Corpus for Question Answering on Electronic Medical Records</title>
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        <namePart type="given">Preethi</namePart>
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    <name type="personal">
        <namePart type="given">Jennifer</namePart>
        <namePart type="family">Liang</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Jun’ichi</namePart>
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    <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 questions-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.</abstract>
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%0 Conference Proceedings
%T emrQA: A Large Corpus for Question Answering on Electronic Medical Records
%A Pampari, Anusri
%A Raghavan, Preethi
%A Liang, Jennifer
%A Peng, Jian
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F pampari-etal-2018-emrqa
%X 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 questions-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.
%R 10.18653/v1/D18-1258
%U https://aclanthology.org/D18-1258
%U https://doi.org/10.18653/v1/D18-1258
%P 2357-2368

Markdown (Informal)

[emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://aclanthology.org/D18-1258) (Pampari et al., EMNLP 2018)

• emrQA: A Large Corpus for Question Answering on Electronic Medical Records (Pampari et al., EMNLP 2018)

ACL

• Anusri Pampari, Preethi Raghavan, Jennifer Liang, and Jian Peng. 2018. emrQA: A Large Corpus for Question Answering on Electronic Medical Records. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2357–2368, Brussels, Belgium. Association for Computational Linguistics.