论文标题
语义相似性的演变 - 调查
Evolution of Semantic Similarity -- A Survey
论文作者
论文摘要
估计文本数据之间的语义相似性是自然语言处理领域（NLP）领域中具有挑战性和开放的研究问题之一。自然语言的多功能性使得难以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。这篇调查文章追踪了此类方法的演变，根据其基本原则为基于知识的，基于语料库的，深神网络的方法和混合方法对它们进行分类。在讨论每种方法的优势和劣势时，本调查提供了现有系统的全面视图，供新研究人员试验和开发创新的思想，以解决语义相似性的问题。
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. In order to address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods, 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.
