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Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task?
Antoine Neuraz, Vincent Looten, Bastien Rance, Nicolas Daniel, Nicolas Garcelon, Leonardo Campillos Llanos, Anita Burgun, Sophie Rosset
1558 - 1559
10.3233/SHTI190533
Research Article
Studies in Health Technology and Informatics
Volume 264: MEDINFO 2019: Health and Wellbeing e-Networks for All
Abstract

We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding and text classification). We compared word embeddings (Fasttext) and language models (ELMo), learned either on the general domain (Wikipedia) or on specialized data (electronic health records, EHR). The best results were obtained with ELMo representations learned on EHR data for one of the two tasks(+7% and +8% of gain in F1-score).

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