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arXiv · 2601.22410

Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking

Imene Kolli·Kai-Robin Lange·Jonas Rieger·Carsten Jentsch
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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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Imene Kolli, Kai-Robin Lange, Jonas Rieger, Carsten Jentsch. 2026-01-29. Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking. https://arxiv.org/abs/2601.22410

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