Statistically Significant Detection of Linguistic Change
This work addresses the challenge of tracking linguistic evolution in large-scale text data, which is incremental as it builds on existing methods for time series and word embeddings.
The paper tackles the problem of detecting statistically significant linguistic shifts in word meaning and usage over time by proposing a computational approach that constructs property time series and applies change point detection algorithms. It demonstrates scalability by analyzing data from Twitter, Amazon reviews, and Google Books, revealing patterns of language change across different media.
We propose a new computational approach for tracking and detecting statistically significant linguistic shifts in the meaning and usage of words. Such linguistic shifts are especially prevalent on the Internet, where the rapid exchange of ideas can quickly change a word's meaning. Our meta-analysis approach constructs property time series of word usage, and then uses statistically sound change point detection algorithms to identify significant linguistic shifts. We consider and analyze three approaches of increasing complexity to generate such linguistic property time series, the culmination of which uses distributional characteristics inferred from word co-occurrences. Using recently proposed deep neural language models, we first train vector representations of words for each time period. Second, we warp the vector spaces into one unified coordinate system. Finally, we construct a distance-based distributional time series for each word to track it's linguistic displacement over time. We demonstrate that our approach is scalable by tracking linguistic change across years of micro-blogging using Twitter, a decade of product reviews using a corpus of movie reviews from Amazon, and a century of written books using the Google Book-ngrams. Our analysis reveals interesting patterns of language usage change commensurate with each medium.