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Temporal Analysis of Language through Neural Language Models

arXiv 1405.3515 ↗First posted 14 May 2014cs.CLcited by 53

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Abstract (arXiv)

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.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 May 2014.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 12; 2023: 6; 2024: 10; 2025: 2; 2026: 6.

53 papers.

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  1. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  2. 2022Entity Cloze By Date: What LMs Know About Unseen Entitiescited by 55
  3. 2021Time Masking for Temporal Language Modelscited by 46
  4. 2021Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corporacited by 21
  5. 2022Temporal Attention for Language Modelscited by 21
  6. 2022Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Changecited by 18
  7. 2021Challenges for Computational Lexical Semantic Changecited by 15
  8. 2021Statistically significant detection of semantic shifts using contextual word embeddingscited by 14
  9. 2022Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slangcited by 14
  10. 2022On the Impact of Temporal Concept Drift on Model Explanationscited by 14
  11. 2021Lexical Semantic Change Discoverycited by 13
  12. 2022HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic Analysiscited by 11
38 more
  1. 2024Robustness of Large Language Models to Perturbations in Textcited by 11
  2. 2023Can Word Sense Distribution Detect Semantic Changes of Words?cited by 10
  3. 2021Opinions are Made to be Changed: Temporally Adaptive Stance Classificationcited by 8
  4. 2023Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddingscited by 8
  5. 2021Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspaperscited by 6
  6. 2023textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swappingcited by 6
  7. 2021Theoretical foundations and limits of word embeddings: what types of meaning can they capture?cited by 5
  8. 2024A Semantic Distance Metric Learning approach for Lexical Semantic Change Detectioncited by 5
  9. 2021Lexical semantic change for Ancient Greek and Latincited by 4
  10. 2021Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detectioncited by 4
  11. 2021Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018cited by 4
  12. 2021On the Universality of Deep Contextual Language Modelscited by 4
  13. 2022A Greek Parliament Proceedings Dataset for Computational Linguistics and Political Analysiscited by 4
  14. 2024Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changescited by 4
  15. 2025SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detectioncited by 4
  16. 2021Monitoring geometrical properties of word embeddings for detecting the emergence of new topicscited by 3
  17. 2022Letters From the Past: Modeling Historical Sound Change Through Diachronic Character Embeddingscited by 3
  18. 2022Predicting Long-Term Citations from Short-Term Linguistic Influencecited by 3
  19. 2024Presence or Absence: Are Unknown Word Usages in Dictionaries?cited by 3
  20. 2024Discovering emergent connections in quantum physics research via dynamic word embeddingscited by 3
  21. 2026Transparent Semantic Change Detection with Dependency-Based Profilescited by 3
  22. 2021How COVID-19 Is Changing Our Language : Detecting Semantic Shift in Twitter Word Embeddingscited by 2
  23. 2023Computational modeling of semantic changecited by 2
  24. 2026ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semanticscited by 2
  25. 2022TransDrift: Modeling Word-Embedding Drift using Transformercited by 1
  26. 2024Tracking Semantic Change in Slovene: A Novel Dataset and Optimal Transport-Based Distancecited by 1
  27. 2024A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Mediacited by 1
  28. 2024Comparative Analysis of Static and Contextual Embeddings for Analyzing Semantic Changes in Medieval Latin Charterscited by 1
  29. 2026Improving Interpretability of Lexical Semantic Change with Neurobiological Featurescited by 1
  30. 2021Towards Using Diachronic Distributed Word Representations as Models of Lexical Developmentcited by 0
  31. 2021DUKweb: Diachronic word representations from the UK Web Archive corpuscited by 0
  32. 2021A Statistical Model of Word Rank Evolutioncited by 0
  33. 2022Domain-Specific Word Embeddings with Structure Predictioncited by 0
  34. 2022Bil-DOS: A Bi-lingual Dialogue Ordering System (for Subway)cited by 0
  35. 2023Contextualized Word Vector-based Methods for Discovering Semantic Differences with No Training nor Word Alignmentcited by 0
  36. 2024Revealing COVID-19's Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twittercited by 0
  37. 2024Words and Action: Modeling Linguistic Leadership in #BlackLivesMatter Communitiescited by 0
  38. 2025GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloadscited by 0

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