OVERVIEW OF DEEP LEARNING MODELS IN NATURAL LANGUAGE PROCESSING

As the key to natural language processing, the models are directly related to the final performance. This paper introduces the models involved in natural language processing. According to the methods of rules and statistics, the traditional natural language processing models were introduced in terms of release time, characteristics, advantages and disadvantages, and scope of application. The neural network was divided into different types according to different technologies, and each type was introduced and its corresponding characteristics were summarized. Two types of improved models based on BERT were introduced in detail and each model was summarized. We analyzed the current challenges faced by natural language processing models and the corresponding solutions. The future work was prospected.