UAlberta at LSCDiscovery: Lexical Semantic Change Detection via Word Sense Disambiguation

Daniela Teodorescu, Spencer von der Ohe, Grzegorz Kondrak


Abstract
We describe our two systems for the shared task on Lexical Semantic Change Discovery in Spanish. For binary change detection, we frame the task as a word sense disambiguation (WSD) problem. We derive sense frequency distributions for target words in both old and modern corpora. We assume that the word semantics have changed if a sense is observed in only one of the two corpora, or the relative change for any sense exceeds a tuned threshold. For graded change discovery, we follow the design of CIRCE (Pömsl and Lyapin, 2020) by combining both static and contextual embeddings. For contextual embeddings, we use XLM-RoBERTa instead of BERT, and train the model to predict a masked token instead of the time period. Our language-independent methods achieve results that are close to the best-performing systems in the shared task.
Anthology ID:
2022.lchange-1.19
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:
180–186
Language:
URL:
https://aclanthology.org/2022.lchange-1.19
DOI:
10.18653/v1/2022.lchange-1.19
Bibkey:
Cite (ACL):
Daniela Teodorescu, Spencer von der Ohe, and Grzegorz Kondrak. 2022. UAlberta at LSCDiscovery: Lexical Semantic Change Detection via Word Sense Disambiguation. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 180–186, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
UAlberta at LSCDiscovery: Lexical Semantic Change Detection via Word Sense Disambiguation (Teodorescu et al., LChange 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lchange-1.19.pdf