Semantic Shift Detection (SSD) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, SSD has been addressed by linguists and social scientists through manual and time-consuming activities. In the recent years, computational approaches based on Natural Language Processing and word embeddings gained increasing attention to automate SSD as much as possible. In particular, over the past three years, significant advancements have been made almost exclusively based on word contextualised embedding models, which can handle the multiple usages/meanings of the words and better capture the related semantic shifts. In this paper, we survey the approaches based on contextualised embeddings for SSD (i.e., CSSDetection) and we propose a classification framework characterised by meaning representation, time-awareness, and learning modality dimensions. The framework is exploited i) to review the measures for shift assessment, ii) to compare the approaches on performance, and iii) to discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about CSSDetection are finally outlined.

翻译：语义变迁检测（Semantic Shift Detection, SSD）是指识别、解释和评估目标词汇含义随时间可能发生变化的任务。传统上，语言学家和社会科学家通过耗时的人工方法开展SSD研究。近年来，基于自然语言处理和词嵌入的计算方法日益受到关注，旨在尽可能实现SSD自动化。特别值得注意的是，过去三年间，研究进展几乎完全围绕基于词语语境化嵌入模型的成果展开，该类模型能够处理词语的多重用法/含义并更精准地捕捉相关语义变迁。本文系统梳理了基于语境化嵌入的SSD方法（即CSSDetection），并提出一个以意义表征、时间感知和学习模态三个维度为特征的分类框架。该框架被用于：i) 评述变迁程度的量化指标，ii) 对比不同方法的性能表现，iii) 探讨当前在可扩展性、可解释性和鲁棒性方面存在的问题。最后，本文对CSSDetection领域的开放性挑战与未来研究方向进行了展望。