@InProceedings{rosenfeld-erk:2018:N18-1,
  author    = {Rosenfeld, Alex  and  Erk, Katrin},
  title     = {Deep Neural Models of Semantic Shift},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/N18-1044}
}