Abstract
Text matching, as a core technology of natural language processing, plays a key role in tasks such as question-and-answer systems and information retrieval. In recent years, the development of neural networks, attention mechanisms, and large-scale language models has significantly contributed to the advancement of text-matching technology. However, the rapid development of the field also poses challenges in fully understanding the overall impact of these technological improvements. This paper aims to provide a concise, yet in-depth, overview of the field of text matching, sorting out the main ideas, problems, and solutions for text-matching methods based on statistical methods and neural networks, as well as delving into matching methods based on large-scale language models, and discussing the related configurations, API applications, datasets, and evaluation methods. In addition, this paper outlines the applications and classifications of text matching in specific domains and discusses the current open problems that are being faced and future research directions, to provide useful references for further developments in the field.
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