Abstract
Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.- Anthology ID:
- Q16-1003
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 4
- Month:
- Year:
- 2016
- Address:
- Cambridge, MA
- Editors:
- Lillian Lee, Mark Johnson, Kristina Toutanova
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 31–45
- Language:
- URL:
- https://aclanthology.org/Q16-1003
- DOI:
- 10.1162/tacl_a_00081
- Cite (ACL):
- Lea Frermann and Mirella Lapata. 2016. A Bayesian Model of Diachronic Meaning Change. Transactions of the Association for Computational Linguistics, 4:31–45.
- Cite (Informal):
- A Bayesian Model of Diachronic Meaning Change (Frermann & Lapata, TACL 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/Q16-1003.pdf