@inproceedings{rosenfeld-erk-2018-deep,
title = "Deep Neural Models of Semantic Shift",
author = "Rosenfeld, Alex and
Erk, Katrin",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1044",
doi = "10.18653/v1/N18-1044",
pages = "474--484",
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.",
}
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%0 Conference Proceedings
%T Deep Neural Models of Semantic Shift
%A Rosenfeld, Alex
%A Erk, Katrin
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F rosenfeld-erk-2018-deep
%X 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.
%R 10.18653/v1/N18-1044
%U https://aclanthology.org/N18-1044
%U https://doi.org/10.18653/v1/N18-1044
%P 474-484
Markdown (Informal)
[Deep Neural Models of Semantic Shift](https://aclanthology.org/N18-1044) (Rosenfeld & Erk, NAACL 2018)
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.