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.
CITATION STYLE
Kashleva, K., Shein, A., Tukhtina, E., & Vydrina, S. (2022). HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery. In LChange 2022 - 3rd International Workshop on Computational Approaches to Historical Language Change 2022, Proceedings of the Workshop (pp. 193–197). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.lchange-1.21
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