New method tracks when and how word meanings change over time
Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change
Computation and Language
Summary
Words can change meaning over time, but it’s hard to tell exactly when and how these changes happen. The authors created a method called CUSP that breaks down word meanings into parts and tracks their movements and changes through time. This method can tell when specific meanings change, how big the change is, and even point to examples in real text. They tested it on synthetic data and real texts, including court opinions, showing it works for tracking detailed word history.
What this means in practice
- •For language technology teams: Track detailed word meaning changes in large text corpora to improve language understanding models.
- •For legal analytics teams: Identify and explain how legal terminology evolves over time in court opinion texts for better legal information retrieval.
Authors
Haruka Ezoe, Ryohei Hisano
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.