Deep Neural Models of Semantic Shift

Alex Rosenfeld, Katrin Erk


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/N18-1044.pdf
Dataset:
 N18-1044.Datasets.zip