2020
EMNLP
EMNLP 2020
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
Abstract
AbstractOne 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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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Healthcare & Medicine and Natural Language Processing
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Keyword Pioneer
— medical abbreviation disambiguation
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Hot Topic Early Bird
— medical domain
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio