计算机技术与发展2023，Vol.33Issue(12)：17-22,6.DOI:10.3969/j.issn.1673-629X.2023.12.003
面向自然语言处理的词向量模型研究综述
Survey of Word Vector Model for Natural Language Processing
摘要
Abstract
Since the1950s,Natural Language Processing(NLP)has made great progress.The early word vector model demonstrates that the study of NLP requires mathematical methods rather than human language rules.After entering the 21st century,the static model which based on deep learning techniques achieves good performance in many tasks.The dynamic model makes use of pre-training techniques and realizes the function of adjusting word vectors according to the context,which brings a milestone breakthrough in the field of NLP.On this basis,the follow-up research extends to various fields,and has been applied on a large scale in real life.We firstly introduce the word vector model and its development history,then analyze the modern models based on deep learning(NNLM,Word2Vec,FastText,Glove,ELMo,GPT,BERT).Secondly,we explain a variety of extended models based on pre-training technology,and describe the current application status of natural language processing technology.Finally,we summarize the main problems at present,and put forward the prospect of future research.关键词
自然语言处理/词向量/深度学习/预训练技术/静态模型/动态模型Key words
natural language processing/word vector/deep learning/pre-training technique/static model/dynamic model分类
信息技术与安全科学引用本文复制引用
安俊秀,蒋思畅..面向自然语言处理的词向量模型研究综述[J].计算机技术与发展,2023,33(12):17-22,6.基金项目
国家社会科学基金项目(22BXW048) （22BXW048）
