Despite the predominance of contextualized embeddings in NLP, approaches to detect semantic change relying on these embeddings and clustering methods underperform simpler counterparts based on static word embeddings. This stems from the poor quality of the clustering methods to produce sense clusters -- which struggle to capture word senses, especially those with low frequency. This issue hinders the next step in examining how changes in word senses in one language influence another. To address this issue, we propose a graph-based clustering approach to capture nuanced changes in both high- and low-frequency word senses across time and languages, including the acquisition and loss of these senses over time. Our experimental results show that our approach substantially surpasses previous approaches in the SemEval2020 binary classification task across four languages. Moreover, we showcase the ability of our approach as a versatile visualization tool to detect semantic changes in both intra-language and inter-language setups. We make our code and data publicly available.

翻译：尽管上下文嵌入在自然语言处理中占据主导地位，但依赖这些嵌入和聚类方法检测语义变化的方法，其性能仍不如基于静态词嵌入的更简单方法。这源于聚类方法在生成语义簇方面的质量欠佳——难以有效捕捉词义，尤其是低频词义。这一问题阻碍了后续探究不同语言间词义变化相互影响的研究。为解决该问题，我们提出一种基于图的聚类方法，用于捕捉跨时间与语言的高频及低频词义的细微变化，包括这些词义随时间的获取与丧失。实验结果表明，我们的方法在SemEval2020二分类任务中，于四种语言上均显著超越以往方法。此外，我们还展示了该方法作为多功能可视化工具，在检测语言内与跨语言语义变化方面的能力。我们的代码和数据已公开提供。