Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking

• URL: http://arxiv.org/abs/2601.22410v1
• Date: Thu, 29 Jan 2026 23:45:58 GMT
• Title: Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking
• Authors: Imene Kolli, Kai-Robin Lange, Jonas Rieger, Carsten Jentsch,
• Abstract summary: We induce a word-centered semantic network that integrates distributional similarity from diachronic Skip-gram embeddings with lexical substitutability from time-specific masked language models.<n>We identify sense-related structure by clustering the peripheral graph, align clusters across time via node overlap, and track change through cluster composition and normalized cluster mass.
• Score: 0.3033221007650832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an interpretable, graph-based framework for analyzing semantic shift in diachronic corpora. For each target word and time slice, we induce a word-centered semantic network that integrates distributional similarity from diachronic Skip-gram embeddings with lexical substitutability from time-specific masked language models. We identify sense-related structure by clustering the peripheral graph, align clusters across time via node overlap, and track change through cluster composition and normalized cluster mass. In an application study on a corpus of New York Times Magazine articles (1980 - 2017), we show that graph connectivity reflects polysemy dynamics and that the induced communities capture contrasting trajectories: event-driven sense replacement (trump), semantic stability with cluster over-segmentation effects (god), and gradual association shifts tied to digital communication (post). Overall, word-centered semantic graphs offer a compact and transparent representation for exploring sense evolution without relying on predefined sense inventories.

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