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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lchange-1.19.pdf