Temporal Analysis of Language through Neural Language Models

Understand

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 obtain 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.