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Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change

arXiv 1906.01688 ↗First posted 4 Jun 2019cs.CLcited by 33

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Abstract (arXiv)

State-of-the-art models of lexical semantic change detection suffer from noise stemming from vector space alignment. We have empirically tested the Temporal Referencing method for lexical semantic change and show that, by avoiding alignment, it is less affected by this noise. We show that, trained on a diachronic corpus, the skip-gram with negative sampling architecture with temporal referencing outperforms alignment models on a synthetic task as well as a manual testset. We introduce a principled way to simulate lexical semantic change and systematically control for possible biases.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2019.

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Citing papers per year: 2021: 15; 2022: 2; 2023: 9; 2024: 3; 2025: 2; 2026: 2.

33 papers.

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  1. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
  2. 2021Time Masking for Temporal Language Modelscited by 46
  3. 2021Let's Play Mono-Poly: BERT Can Reveal Words' Polysemy Level and Partitionability into Sensescited by 39
  4. 2021Explaining and Improving BERT Performance on Lexical Semantic Change Detectioncited by 34
  5. 2023A Survey on Contextualised Semantic Shift Detectioncited by 22
  6. 2022Temporal Attention for Language Modelscited by 21
  7. 2021Challenges for Computational Lexical Semantic Changecited by 16
  8. 2023Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligencecited by 15
  9. 2021Conceptual similarity and communicative need shape colexification: an experimental studycited by 14
  10. 2021Statistically significant detection of semantic shifts using contextual word embeddingscited by 14
  11. 2021Lexical Semantic Change Discoverycited by 13
  12. 2023Once Upon a textitTime in textitGraph: Relative-Time Pretraining for Complex Temporal Reasoningcited by 11
21 more
  1. 2023Can Word Sense Distribution Detect Semantic Changes of Words?cited by 10
  2. 2023Unsupervised Semantic Variation Prediction using the Distribution of Sibling Embeddingscited by 8
  3. 2021Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspaperscited by 6
  4. 2023textitSwap and Predict -- Predicting the Semantic Changes in Words across Corpora by Context Swappingcited by 6
  5. 2024A Semantic Distance Metric Learning approach for Lexical Semantic Change Detectioncited by 5
  6. 2021Lexical semantic change for Ancient Greek and Latincited by 4
  7. 2021Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detectioncited by 4
  8. 2021From Plenipotentiary to Puddingless: Users and Uses of New Words in Early English Letterscited by 4
  9. 2021Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018cited by 4
  10. 2025SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detectioncited by 4
  11. 2022Predicting Long-Term Citations from Short-Term Linguistic Influencecited by 3
  12. 2024Syntactic Language Change in English and German: Metrics, Parsers, and Convergencescited by 3
  13. 2024The LSCD Benchmark: a Testbed for Diachronic Word Meaning Taskscited by 3
  14. 2023Computational modeling of semantic changecited by 2
  15. 2025LSC-Eval: A General Framework to Evaluate Methods for Assessing Dimensions of Lexical Semantic Change Using LLM-Generated Synthetic Datacited by 2
  16. 2021SuperSim: a test set for word similarity and relatedness in Swedishcited by 1
  17. 2021Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inferencecited by 1
  18. 2023Contextualized Word Vector-based Methods for Discovering Semantic Differences with No Training nor Word Alignmentcited by 0
  19. 2023An Embedded Diachronic Sense Change Model with a Case Study from Ancient Greekcited by 0
  20. 2026Rethinking Metrics for Lexical Semantic Change Detectioncited by 0
  21. 2026The BD-LSC Dataset: Facilitating the Benchmarking of Models for Lexical Semantic Change Detection in Slang and Standard Usagecited by 0