HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery

Kseniia Kashleva, Alexander Shein, Elizaveta Tukhtina, Svetlana Vydrina


Abstract
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.
Anthology ID:
2022.lchange-1.21
Original:
2022.lchange-1.21v1
Version 2:
2022.lchange-1.21v2
Volume:
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Nina Tahmasebi, Syrielle Montariol, Andrey Kutuzov, Simon Hengchen, Haim Dubossarsky, Lars Borin
Venue:
LChange
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
193–197
Language:
URL:
https://aclanthology.org/2022.lchange-1.21
DOI:
10.18653/v1/2022.lchange-1.21
Bibkey:
Cite (ACL):
Kseniia Kashleva, Alexander Shein, Elizaveta Tukhtina, and Svetlana Vydrina. 2022. HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 193–197, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery (Kashleva et al., LChange 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-3/2022.lchange-1.21.pdf