自然语言处理的深度学习模型综述  

OVERVIEW OF DEEP LEARNING MODELS IN NATURAL LANGUAGE PROCESSING

作　　者：何雪锋[1,2] 周洁 陈德光 廖海 He Xuefeng;Zhou Jie;Chen Deguang;Liao Hai(School of Software,Sichuan Vocational College of Information Technology,Guangyuan 628000,Sichuan,China;School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,Ningxia,China)

机构地区：[1]四川信息职业技术学院软件学院,四川广元628000 [2]北方民族大学计算机科学与工程学院,宁夏银川750021

出　　处：《计算机应用与软件》2025年第2期1-19,101,共20页Computer Applications and Software

基　　金：国家自然科学基金项目(61862001,61762003,61462002);宁夏高等学校一流学科建设(数学)项目(NXYLXK2017B09);四川移动应用开发协同创新中心科研项目(2021KC10,2020KC24)。

摘　　要：模型作为自然语言处理的关键,直接关系到最终性能。该文介绍自然语言处理中涉及到的模型。按照规则与统计的方法从发布时间、特点、优缺点与适用范围等方面对传统自然语言处理模型进行介绍;重点将神经网络依据不同的技术划分为不同的类型,对每种类型进行介绍并总结其相应特性;对以BERT为基础的两大类改进模型进行具体介绍并对每种模型进行归纳;分析目前自然语言处理模型面临的挑战与对应的解决办法;对未来工作进行展望。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.

关 键 词：自然语言处理 语言模型 人工智能 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]