Abstract
Diachronic Word Sense Induction (DWSI) is the task of inducing the temporal representations of a word meaning from the context, as a set of senses and their prevalence over time. We introduce two new models for DWSI, based on topic modelling techniques: one is based on Hierarchical Dirichlet Processes (HDP), a nonparametric model; the other is based on the Dynamic Embedded Topic Model (DETM), a recent dynamic neural model. We evaluate these models against two state of the art DWSI models, using a time-stamped labelled dataset from the biomedical domain. We demonstrate that the two proposed models perform better than the state of the art. In particular, the HDP-based model drastically outperforms all the other models, including the dynamic neural model.- Anthology ID:
- 2023.findings-acl.567
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8908–8925
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.567
- DOI:
- 10.18653/v1/2023.findings-acl.567
- Cite (ACL):
- Ashjan Alsulaimani and Erwan Moreau. 2023. Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8908–8925, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method (Alsulaimani & Moreau, Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.findings-acl.567.pdf