Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 32 min ago
PreprintarXiv licencearXiv

Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change

Abstract

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.

Related

  1. Foundations of Stochastic Lexical Calculus: Semantic Descent and Random Dynamics on Probability Simplices
    Matthew F Dixon
    arXivPreprintCC BY 4.0Full textAlgebra & number theoryProbabilityarXiv
  2. A Mathematical Modeling Framework for Semantic–Temporal Graph Learning in Log Anomaly Detection
    Shuo Gao, Xiaoyan Wang
    MathematicsPeer-reviewedCC BY 4.0General mathematicsCrossref
  3. Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes
    Carrie J. Lei-Cramer, Jian Cao, Matthias Katzfuss
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  4. Flexible Intensities Matter: A Comprehensive Re-evaluation of Hawkes Processes and Neural Temporal Point Processes
    Hendrik Mehrtens, Florian Buettner, Oliver Stegle
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  5. Temporal-Logical Stochastic Processes
    Venkatraman Renganathan
    arXivPreprintarXiv licenceFull textProbabilityarXiv
BibTeX
@misc{ezoe2026coupled,
  title = {{Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change}},
  author = {Haruka Ezoe and Ryohei Hisano},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.30974},
  eprint = {2609.30974},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.30974},
}
RIS
TY  - UNPB
TI  - Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change
AU  - Haruka Ezoe
AU  - Ryohei Hisano
PY  - 2026
DA  - 2026/09/25
PB  - arXiv
DO  - 10.48550/arxiv.2609.30974
UR  - https://arxiv.org/abs/2609.30974
AB  - 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.
ER  -