Automatic semantic change methods try to identify the changes that appear
over time in the meaning of words by analyzing their usage in diachronic
corpora. In this paper, we analyze different strategies to create static and
contextual word embedding models, i.e., Word2Vec and ELMo, on real-world
English and Romanian datasets. To test our pipeline and determine the
performance of our models, we first evaluate both word embedding models on an
English dataset (SEMEVAL-CCOHA). Afterward, we focus our experiments on a
Romanian dataset, and we underline different aspects of semantic changes in
this low-resource language, such as meaning acquisition and loss. The
experimental results show that, depending on the corpus, the most important
factors to consider are the choice of model and the distance to calculate a
score for detecting semantic change.

自动语义变化方法旨在通过分析词语在历时语料库中的使用来识别其含义随时间的变化。本文分析了在真实的英语和罗马尼亚数据集上创建静态和上下文词嵌入模型（Word2Vec 和 ELMo）的不同策略。通过对英语数据集（SEMEVAL-CCOHA）进行评估，并针对罗马尼亚数据集进行实验来确定模型的性能，并突出该低资源语言中语义变化的不同方面，如含义的获取与丧失。实验结果表明，在选择模型和计算语义变化得分的距离方面，取决于语料库，这是最重要的因素。