![]() Unfortunately, research and clinical applications in this area have suffered from a lack of publicly available pretraining data due to privacy restrictions, and a glut of non-standard abbreviations in the data that is available. ![]() ![]() Nowhere is correct terminology more critical than in medicine and health care, where text mining and natural language processing can build deep learning models for diagnosis prediction and other tasks. In an EMNLP 2020 Clinical NLP workshop last week, a Montreal-based research team introduced a large medical text dataset designed to boost abbreviation disambiguation in the medical domain.
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