emrQA: A large corpus for question answering on electronic medical records

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Pampari, Anusri

Contributors dc:contributor

• Peng, Jian

• Electronic Medical Records, Question Answering, Logical Forms, Semantic Parsing, Dataset Generation, Closed Domain, i2b2

Statement dc:rights

• Accepted at Conference on Empirical Methods in Natural Language Processing (EMNLP) 2018

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/102500

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/102500