51 research outputs found
Temporal Analysis of Language through Neural Language Models
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
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