Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change
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Abstract (arXiv)
Lexical semantic change is usually summarized by a scalar distance between independently sampled period distributions. This measures how much a word changed, but does not reveal when it changed, which mechanisms and component movements carried the change, or which usages support the attribution. We introduce Coupled Usage--Sense Processes (CUSP), which derives these answers from a single marginal preserving temporal process. A hierarchical coupling relates contextual distributions through latent usage components, while Markov composition makes adjacent and longer span correspondences compatible. Displacement operators quantify change magnitude and timing, split variation exactly between movement of component centers and reorganization within components, and attribute it to transported component pairs. Word-local modes resolve distinct directions of change and their activity over time, while representative passages from attributed components ground the analysis in text. Under a Gaussian mixture specialization, we prove parametric recovery of the operators and squared distances. Synthetic experiments support the predicted rate. CUSP remains competitive on English and German DWUG and recovers controlled Janus profiles while maintaining compositionally coherent transport. A large corpus of US court opinions demonstrates transition, mode, and passage attribution in unlabeled natural text. CUSP thus makes magnitude, timing, mechanism, movement, modes, and textual evidence compatible views of one lexical history.
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References · 20
- 2018Computational Optimal Transportcited by 780
- 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 221
- 2017All-but-the-Top: Simple and Effective Postprocessing for Word Representationscited by 212
- 2021All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Qualitycited by 156
- 2020Analysing Lexical Semantic Change with Contextualised Word Representationscited by 92
- 2019A Wasserstein-type distance in the space of Gaussian Mixture Modelscited by 75
- 2019Hierarchical Optimal Transport for Document Representationcited by 53
- 2017Dynamic Word Embeddingscited by 51
- 2017Optimal transport for Gaussian mixture modelscited by 45
- 2021DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languagescited by 26
- 2024A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Changecited by 21
- 2013Model Selection for Gaussian Mixture Modelscited by 6
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- 2023Distribution-on-Distribution Regression with Wasserstein Metric: Multivariate Gaussian Casecited by 5
- 2024Investigating the Contextualised Word Embedding Dimensions Specified for Contextual and Temporal Semantic Changescited by 4
- 2025SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detectioncited by 4
- 2024Quantifying Lexical Semantic Shift via Unbalanced Optimal Transportcited by 3
- 2025Analyzing Continuous Semantic Shifts with Diachronic Word Similarity Matricescited by 3
- 2026Word-Centered Semantic Graphs for Interpretable Diachronic Sense Trackingcited by 1
- 2026Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Pointscited by 1
- 2026SynFlow: A Multidimensional Diachronic Semantic Analysis Toolkitcited by 1
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