  Lexical semantic change detection (also known as semantic shift tracing) is a
task of identifying words that have changed their meaning over time.
Unsupervised semantic shift tracing, focal point of SemEval2020, is
particularly challenging. Given the unsupervised setup, in this work, we
propose to identify clusters among different occurrences of each target word,
considering these as representatives of different word meanings. As such,
disagreements in obtained clusters naturally allow to quantify the level of
semantic shift per each target word in four target languages. To leverage this
idea, clustering is performed on contextualized (BERT-based) embeddings of word
occurrences. The obtained results show that our approach performs well both
measured separately (per language) and overall, where we surpass all provided
SemEval baselines.
