Abstract
This paper explores lexical meaning changes in a new dataset, which includes tweets from before and after the COVID-related lockdown in April 2020. We use this dataset to evaluate traditional and more recent unsupervised approaches to lexical semantic change that make use of contextualized word representations based on the BERT neural language model to obtain representations of word usages. We argue that previous models that encode local representations of words cannot capture global context shifts such as the context shift of face masks since the pandemic outbreak. We experiment with neural topic models to track context shifts of words. We show that this approach can reveal textual associations of words that go beyond their lexical meaning representation. We discuss future work and how to proceed capturing the pragmatic aspect of meaning change as opposed to lexical semantic change.- Anthology ID:
- 2022.lchange-1.14
- 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:
- 131–139
- Language:
- URL:
- https://aclanthology.org/2022.lchange-1.14
- DOI:
- 10.18653/v1/2022.lchange-1.14
- Cite (ACL):
- Olga Kellert and Md Mahmud Uz Zaman. 2022. Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 131–139, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Using neural topic models to track context shifts of words: a case study of COVID-related terms before and after the lockdown in April 2020 (Kellert & Mahmud Uz Zaman, LChange 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.lchange-1.14.pdf