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Word embedding for French natural language in healthcare: a comparative study (Preprint)
2018
JMIR Medical Informatics
Word embedding technologies, a set of language modeling and feature learning techniques in natural language processing (NLP), are now used in a wide range of applications. However, no formal evaluation and comparison have been made on the ability of each of the 3 current most famous unsupervised implementations (Word2Vec, GloVe, and FastText) to keep track of the semantic similarities existing between words, when trained on the same dataset. The aim of this study was to compare embedding
doi:10.2196/12310
pmid:31359873
pmcid:PMC6690161
fatcat:jntc3ylh3rdwzkzx4ivhernsha