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Lexical semantic change detection (also known as semantic shift tracing) is atask of identifying words that have changed their meaning over time.Unsupervised semantic shift tracing, focal point of SemEval2020, isparticularly challenging. Given the unsupervised setup, in this work, wepropose 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 ofsemantic shift per each target word in four target languages. To leverage thisidea, clustering is performed on contextualized (BERT-based) embeddings of wordoccurrences. The obtained results show that our approach performs well bothmeasured separately (per language) and overall, where we surpass all providedSemEval baselines.

SST-BERT at SemEval-2020 Task 1: Semantic Shift Tracing by Clustering in BERT-based Embedding Spaces