文本分类中Prompt Learning方法研究综述

Text Classification:Comprehensive Review of Prompt Learning Methods

文本分类是自然语言处理中的一项基础任务,在情感分析、新闻分类等领域具有重要应用。相较于传统的机器学习和深度学习模型,提示学习可以在数据不足的情况下通过构建提示来进行文本分类。近年来,GPT-3的出现推动了提示学习方法的发展,并且在文本分类领域取得了显著的进展。对以往的文本分类方法进行简要梳理,分析其存在的问题与不足;阐述了提示学习的发展进程,以及构建提示模板的方法,并对用于文本分类的提示学习方法研究及成果进行了介绍和总结。最后,对提示学习在文本分类领域的发展趋势和有待进一步研究的难点进行了总结和展望。

Text classification is a basic task in natural language processing,which has important applications in sentiment analysis,news classification and other fields.Compared with traditional machine learning and deep learning models,prompt learning can construct prompts for text classification in the case of insufficient data.In recent years,the emergence of GPT-3 has promoted the development of cue learning methods,and has made significant progress in the field of text classification.Firstly,this paper briefly combs the process of previous text classification methods and analyzes their existing problems and shortcomings.Secondly,it expounds the development process of cue learning and the method of constructing cue templates,and introduces and summarizes the research and results of cue learning methods for text classification.Finally,the development trend and difficulties to be further studied in the field of text classification are summarized and prospected.