MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
arXiv:2012.13978 · doi:10.18653/v1/2020.clinicalnlp-1.15
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.
arXiv commentEMNLP 2020 Clinical NLP
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