Semantic Change Detection (SCD) of words is an important task for various NLP
applications that must make time-sensitive predictions. Some words are used
over time in novel ways to express new meanings, and these new meanings
establish themselves as novel senses of existing words. On the other hand, Word
Sense Disambiguation (WSD) methods associate ambiguous words with sense ids,
depending on the context in which they occur. Given this relationship between
WSD and SCD, we explore the possibility of predicting whether a target word has
its meaning changed between two corpora collected at different time steps, by
comparing the distributions of senses of that word in each corpora. For this
purpose, we use pretrained static sense embeddings to automatically annotate
each occurrence of the target word in a corpus with a sense id. Next, we
compute the distribution of sense ids of a target word in a given corpus.
Finally, we use different divergence or distance measures to quantify the
semantic change of the target word across the two given corpora. Our
experimental results on SemEval 2020 Task 1 dataset show that word sense
distributions can be accurately used to predict semantic changes of words in
English, German, Swedish and Latin.

通过比较两个不同时期收集的语料库中目标词义的分布，利用预训练的静态感知嵌入自动标注每个语料库中目标词出现的感知 id，并使用不同的差异或距离度量来量化目标词在两个给定语料库中的语义变化，实验结果在 SemEval 2020 任务 1 数据集上显示出，可以准确地利用词义分布来预测英语、德语、瑞典语和拉丁语中词的语义变化。