@inproceedings{kashleva-etal-2022-black,
title = "{HSE} at {LSCD}iscovery in {S}panish: Clustering and Profiling for Lexical Semantic Change Discovery",
author = "Kashleva, Kseniia and
Shein, Alexander and
Tukhtina, Elizaveta and
Vydrina, Svetlana",
editor = "Tahmasebi, Nina and
Montariol, Syrielle and
Kutuzov, Andrey and
Hengchen, Simon and
Dubossarsky, Haim and
Borin, Lars",
booktitle = "Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.lchange-1.21",
doi = "10.18653/v1/2022.lchange-1.21",
pages = "193--197",
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.",
}
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%0 Conference Proceedings
%T HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery
%A Kashleva, Kseniia
%A Shein, Alexander
%A Tukhtina, Elizaveta
%A Vydrina, Svetlana
%Y Tahmasebi, Nina
%Y Montariol, Syrielle
%Y Kutuzov, Andrey
%Y Hengchen, Simon
%Y Dubossarsky, Haim
%Y Borin, Lars
%S Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kashleva-etal-2022-black
%X 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.
%R 10.18653/v1/2022.lchange-1.21
%U https://aclanthology.org/2022.lchange-1.21
%U https://doi.org/10.18653/v1/2022.lchange-1.21
%P 193-197
Markdown (Informal)
[HSE at LSCDiscovery in Spanish: Clustering and Profiling for Lexical Semantic Change Discovery](https://aclanthology.org/2022.lchange-1.21) (Kashleva et al., LChange 2022)
ACL