Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

中文翻译：

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语义相似性的演变——一项调查

估计文本数据之间的语义相似性是自然语言处理（NLP）领域具有挑战性和开放性的研究问题之一。自然语言的多功能性使得很难定义基于规则的方法来确定语义相似性度量。为了解决这个问题，多年来已经提出了各种语义相似性方法。这篇综述文章追溯了这些方法的演变，从传统的 NLP 技术（如基于内核的方法）到最近关于基于转换器的模型的研究工作，并根据其基本原理将它们分类为基于知识的、基于语料库的、基于深度神经的基于网络的方法和混合方法。讨论每种方法的优缺点，