2020

Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset

Yue, Xiang, Gutierrez, Bernal Jimenez, Sun, Huan

Understand

Machine reading comprehension has made great progress in recent years owing to large-scale annotated datasets.

In the clinical domain, however, creating such datasets is quite difficult due to the domain expertise required for annotation.
Recently, Pampari et al.
(EMNLP'18) tackled this issue by using expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering (QA) based on clinical notes.

Built on

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