Deep Neural Models of Semantic Shift
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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