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Alsentzer, E., Murphy, J.R., Boag, W., et al. (2019) Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, 72-78. arXiv:1904.03323 https://doi.org/10.18653/v1/W19-1909

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• TITLE: Summary of Research Methods on Pre-Training Models of Natural Language Processing

  AUTHORS: Yu Xiao, Zhezhi Jin

  KEYWORDS: Natural Language Processing, Pre-Training Model, Language Model, Self-Training Model

  JOURNAL NAME: Open Access Library Journal, Vol.8 No.7, July 1, 2021

  ABSTRACT: In recent years, deep learning technology has been widely used and developed. In natural language processing tasks, pre-training models have been more widely used. Whether it is sentence extraction or sentiment analysis of text, the pre-training model plays a very important role. The use of a large-scale corpus for unsupervised pre-training of models has proven to be an excellent and effective way to provide models. This article summarizes the existing pre-training models and sorts out the improved models and processing methods of the relatively new pre-training models, and finally summarizes the challenges and prospects of the current pre-training models.