# A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains

arXiv: https://arxiv.org/abs/1906.02979 · HTML: https://mlsanity.com/paper/1906.02979/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 30 of 31)

- [Time Masking for Temporal Language Models](https://mlsanity.com/paper/2110.06366/) (2021)
- [Explaining and Improving BERT Performance on Lexical Semantic Change Detection](https://mlsanity.com/paper/2103.07259/) (2021)
- [Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora](https://mlsanity.com/paper/2112.14330/) (2021)
- [Temporal Attention for Language Models](https://mlsanity.com/paper/2202.02093/) (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)
- [Lexical Semantic Change Discovery](https://mlsanity.com/paper/2106.03111/) (2021)
- [LSCDiscovery: A shared task on semantic change discovery and detection in Spanish](https://mlsanity.com/paper/2205.06691/) (2022)
- [Robustness of Large Language Models to Perturbations in Text](https://mlsanity.com/paper/2407.08989/) (2024)
- [NorDiaChange: Diachronic Semantic Change Dataset for Norwegian](https://mlsanity.com/paper/2201.05123/) (2022)
- [Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change](https://mlsanity.com/paper/2204.05717/) (2022)
- [Evolving linguistic divergence on polarizing social media](https://mlsanity.com/paper/2309.01659/) (2023)
- [Survey in Characterizing Semantic Change](https://mlsanity.com/paper/2402.19088/) (2024)
- [Lexical semantic change for Ancient Greek and Latin](https://mlsanity.com/paper/2101.09069/) (2021)
- [Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection](https://mlsanity.com/paper/2101.09368/) (2021)
- [DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature](https://mlsanity.com/paper/2107.01198/) (2021)
- [On the Universality of Deep Contextual Language Models](https://mlsanity.com/paper/2109.07140/) (2021)
- [The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks](https://mlsanity.com/paper/2404.00176/) (2024)
- [Mixed-effects transformers for hierarchical adaptation](https://mlsanity.com/paper/2205.01749/) (2022)
- [Large Language Models on Lexical Semantic Change Detection: An Evaluation](https://mlsanity.com/paper/2312.06002/) (2023)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)
- [Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks](https://mlsanity.com/paper/2102.00290/) (2021)
- [Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distance](https://mlsanity.com/paper/2402.16596/) (2024)
- [Improving Interpretability of Lexical Semantic Change with Neurobiological Features](https://mlsanity.com/paper/2602.09760/) (2026)
- [DUKweb: Diachronic word representations from the UK Web Archive corpus](https://mlsanity.com/paper/2107.01076/) (2021)
- [Text analysis and deep learning: A network approach](https://mlsanity.com/paper/2110.04151/) (2021)
- [A Tale of Two Laws of Semantic Change: Predicting Synonym Changes with Distributional Semantic Models](https://mlsanity.com/paper/2305.19143/) (2023)
- [Values That Are Explicitly Present in Fairy Tales: Comparing Samples from German, Italian and Portuguese Traditions](https://mlsanity.com/paper/2402.08318/) (2024)
- [Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings](https://mlsanity.com/paper/2506.13569/) (2025)
- [Rethinking Metrics for Lexical Semantic Change Detection](https://mlsanity.com/paper/2602.15716/) (2026)

## Abstract (arXiv)

We perform an interdisciplinary large-scale evaluation for detecting lexical semantic divergences in a diachronic and in a synchronic task: semantic sense changes across time, and semantic sense changes across domains. Our work addresses the superficialness and lack of comparison in assessing models of diachronic lexical change, by bringing together and extending benchmark models on a common state-of-the-art evaluation task. In addition, we demonstrate that the same evaluation task and modelling approaches can successfully be utilised for the synchronic detection of domain-specific sense divergences in the field of term extraction.