A Paraphrase Generation System for EHR Question Answering

Sarvesh Soni, Kirk Roberts


Abstract
This paper proposes a dataset and method for automatically generating paraphrases for clinical questions relating to patient-specific information in electronic health records (EHRs). Crowdsourcing is used to collect 10,578 unique questions across 946 semantically distinct paraphrase clusters. This corpus is then used with a deep learning-based question paraphrasing method utilizing variational autoencoder and LSTM encoder/decoder. The ultimate use of such a method is to improve the performance of automatic question answering methods for EHRs.
Anthology ID:
W19-5003
Volume:
Proceedings of the 18th BioNLP Workshop and Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
20–29
Language:
URL:
https://aclanthology.org/W19-5003
DOI:
10.18653/v1/W19-5003
Bibkey:
Cite (ACL):
Sarvesh Soni and Kirk Roberts. 2019. A Paraphrase Generation System for EHR Question Answering. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 20–29, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
A Paraphrase Generation System for EHR Question Answering (Soni & Roberts, BioNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-5003.pdf