MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

Zhi Wen, Xing Han Lu, Siva Reddy


Abstract
One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.
Anthology ID:
2020.clinicalnlp-1.15
Volume:
Proceedings of the 3rd Clinical Natural Language Processing Workshop
Month:
November
Year:
2020
Address:
Online
Venues:
ClinicalNLP | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
130–135
Language:
URL:
https://www.aclweb.org/anthology/2020.clinicalnlp-1.15
DOI:
10.18653/v1/2020.clinicalnlp-1.15
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clinicalnlp-1.15.pdf
Optional supplementary material:
 2020.clinicalnlp-1.15.OptionalSupplementaryMaterial.zip