Key points are not available for this paper at this time.
We provide a method for automatically detecting change in language across time through a chronologically trained neural language model. We train the model on the Google Books Ngram corpus to ob-tain word vector representations specific to each year, and identify words that have changed significantly from 1900 to 2009. The model identifies words such as cell and gay as having changed during that time period. The model simultaneously identifies the specific years during which such words underwent change. 1
Kim et al. (Wed,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: