# Statistically Significant Detection of Linguistic Change

arXiv: https://arxiv.org/abs/1411.3315 · HTML: https://mlsanity.com/paper/1411.3315/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2014.

## Built on by (most cited first, 30 of 82)

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- [Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views](https://mlsanity.com/paper/2302.12297/) (2023)
- [Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change](https://mlsanity.com/paper/2210.17127/) (2022)
- [Challenges for Computational Lexical Semantic Change](https://mlsanity.com/paper/2101.07668/) (2021)
- [Emblaze: Illuminating Machine Learning Representations through Interactive Comparison of Embedding Spaces](https://mlsanity.com/paper/2202.02641/) (2022)
- [Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang](https://mlsanity.com/paper/2203.04651/) (2022)
- [Statistically significant detection of semantic shifts using contextual word embeddings](https://mlsanity.com/paper/2104.03776/) (2021)
- [Assessing the Reliability of Word Embedding Gender Bias Measures](https://mlsanity.com/paper/2109.04732/) (2021)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [Three-part diachronic semantic change dataset for Russian](https://mlsanity.com/paper/2106.08294/) (2021)
- [HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic Analysis](https://mlsanity.com/paper/2202.03612/) (2022)
- [Reducing Retraining by Recycling Parameter-Efficient Prompts](https://mlsanity.com/paper/2208.05577/) (2022)
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- [Can Word Sense Distribution Detect Semantic Changes of Words?](https://mlsanity.com/paper/2310.10400/) (2023)
- [Opinions are Made to be Changed: Temporally Adaptive Stance Classification](https://mlsanity.com/paper/2108.12476/) (2021)
- [LMSOC: An Approach for Socially Sensitive Pretraining](https://mlsanity.com/paper/2110.10319/) (2021)
- [Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddings](https://mlsanity.com/paper/2305.08654/) (2023)
- [Neural Language Models for Nineteenth-Century English](https://mlsanity.com/paper/2105.11321/) (2021)
- [NorDiaChange: Diachronic Semantic Change Dataset for Norwegian](https://mlsanity.com/paper/2201.05123/) (2022)
- [Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers](https://mlsanity.com/paper/2103.07538/) (2021)
- [textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swapping](https://mlsanity.com/paper/2310.10397/) (2023)
- [Theoretical foundations and limits of word embeddings: what types of meaning can they capture?](https://mlsanity.com/paper/2107.10413/) (2021)
- [Writing Assistants Should Model Social Factors of Language](https://mlsanity.com/paper/2303.16275/) (2023)
- [A Semantic Distance Metric Learning approach for Lexical Semantic Change Detection](https://mlsanity.com/paper/2403.00226/) (2024)
- [Alignment and stability of embeddings: measurement and inference improvement](https://mlsanity.com/paper/2101.07251/) (2021)

## Abstract (arXiv)

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.