This paper describes the methods used for lexical semantic change discovery in Spanish. We tried the method based on BERT embeddings with clustering, the method based on grammatical profiles and the grammatical profiles method enhanced with permutation tests. BERT embeddings with clustering turned out to show the best results for both graded and binary semantic change detection outperforming the baseline. Our best submission for graded discovery was the 3rd best result, while for binary detection it was the 2nd place (precision) and the 7th place (both F1-score and recall). Our highest precision for binary detection was 0.75 and it was achieved due to improving grammatical profiling with permutation tests.

This paper describes our methods for temporal meaning shift detection, implemented during the TempoWiC shared task. We present two systems: with and without time span data usage. Our approaches are based on the language models fine-tuned for Twitter domain. Both systems outperformed all the competition’s baselines except TimeLMs-SIM. Our best submission achieved the macro-F1 score of 70.09% and took the 7th place. This result was achieved by using diachronic language models from the TimeLMs project.