Abstract
Diachronic distributional models track changes in word use over time. In this paper, we propose a deep neural network diachronic distributional model. Instead of modeling lexical change via a time series as is done in previous work, we represent time as a continuous variable and model a word’s usage as a function of time. Additionally, we have also created a novel synthetic task which measures a model’s ability to capture the semantic trajectory. This evaluation quantitatively measures how well a model captures the semantic trajectory of a word over time. Finally, we explore how well the derivatives of our model can be used to measure the speed of lexical change.- Anthology ID:
- N18-1044
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 474–484
- Language:
- URL:
- https://aclanthology.org/N18-1044
- DOI:
- 10.18653/v1/N18-1044
- Cite (ACL):
- Alex Rosenfeld and Katrin Erk. 2018. Deep Neural Models of Semantic Shift. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 474–484, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Deep Neural Models of Semantic Shift (Rosenfeld & Erk, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/N18-1044.pdf