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Improving Diachronic Word Sense Induction with a Nonparametric Bayesian method

Ashjan Alsulaimani, Erwan Moreau


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.567.pdf
Video:
 https://aclanthology.org/2023.findings-acl.567.mp4