MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining

• URL: http://arxiv.org/abs/2012.13978v1
• Date: Sun, 27 Dec 2020 17:17:39 GMT
• Title: MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
• Authors: Zhi Wen, Xing Han Lu, Siva Reddy
• Abstract summary: We present MeDAL, a large medical text dataset curated for abbreviation disambiguation. 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.
• Score: 5.807159674193696
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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