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Leveraging Contextual Embeddings for Detecting Diachronic Semantic Shift

arXiv 1912.01072 ↗First posted 2 Dec 2019cs.CLcited by 37

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Abstract (arXiv)

We propose a new method that leverages contextual embeddings for the task of diachronic semantic shift detection by generating time specific word representations from BERT embeddings. The results of our experiments in the domain specific LiverpoolFC corpus suggest that the proposed method has performance comparable to the current state-of-the-art without requiring any time consuming domain adaptation on large corpora. The results on the newly created Brexit news corpus suggest that the method can be successfully used for the detection of a short-term yearly semantic shift. And lastly, the model also shows promising results in a multilingual settings, where the task was to detect differences and similarities between diachronic semantic shifts in different languages.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Dec 2019.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 7; 2024: 8; 2025: 3; 2026: 7.

37 papers.

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  1. 2021All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Qualitycited by 150
  2. 2021Time Masking for Temporal Language Modelscited by 46
  3. 2021Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corporacited by 21
  4. 2022Temporal Attention for Language Modelscited by 21
  5. 2023A Survey on Contextualised Semantic Shift Detectioncited by 21
  6. 2024A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Changecited by 21
  7. 2022Contextualized language models for semantic change detection: lessons learnedcited by 20
  8. 2021Statistically significant detection of semantic shifts using contextual word embeddingscited by 14
  9. 2022Discovering Differences in the Representation of People using Contextualized Semantic Axescited by 12
  10. 2024Robustness of Large Language Models to Perturbations in Textcited by 11
  11. 2023Can Word Sense Distribution Detect Semantic Changes of Words?cited by 10
  12. 2023Substitution-based Semantic Change Detection using Contextual Embeddingscited by 9
25 more
  1. 2022What company do words keep? Revisiting the distributional semantics of J.R. Firth & Zellig Harriscited by 8
  2. 2023Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddingscited by 8
  3. 2022Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Changecited by 7
  4. 2024Time Machine GPTcited by 7
  5. 2024Analyzing Semantic Change through Lexical Replacementscited by 7
  6. 2023textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swappingcited by 6
  7. 2023Social Meme-ing: Measuring Linguistic Variation in Memescited by 5
  8. 2024A Semantic Distance Metric Learning approach for Lexical Semantic Change Detectioncited by 5
  9. 2021Monitoring geometrical properties of word embeddings for detecting the emergence of new topicscited by 3
  10. 2024Tracing the Development of the Virtual Particle Concept Using Semantic Change Detectioncited by 3
  11. 2021The challenges of temporal alignment on Twitter during crisescited by 2
  12. 2023When Dialects Collide: How Socioeconomic Mixing Affects Language Usecited by 2
  13. 2024The LSCD Benchmark: a Testbed for Diachronic Word Meaning Taskscited by 2
  14. 2025Rethinking Word Similarity: Semantic Similarity through Classification Confusioncited by 2
  15. 2024Comparative Analysis of Static and Contextual Embeddings for Analyzing Semantic Changes in Medieval Latin Charterscited by 1
  16. 2025Identifying Emerging Concepts in Large Corporacited by 1
  17. 2026Improving Interpretability of Lexical Semantic Change with Neurobiological Featurescited by 1
  18. 2026DHPLT: large-scale multilingual diachronic corpora and word representations for semantic change modellingcited by 1
  19. 2021Diachronic Text Mining Investigation of Therapeutic Candidates for COVID-19cited by 0
  20. 2025Hatevolution: What Static Benchmarks Don't Tell Uscited by 0
  21. 2026From sunblock to softblock: Analyzing the correlates of neology in published writing and on social mediacited by 0
  22. 2026Rethinking Metrics for Lexical Semantic Change Detectioncited by 0
  23. 2026Between Century and Poet: Graph-Based Lexical Semantic Change in Persian Poetrycited by 0
  24. 2026Computational conceptual history of scientific concepts: From early digital methods to LLMscited by 0
  25. 2026Dynamics of Meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhalacited by 0