Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912861774675968 |
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| author | Kolli, Imene Lange, Kai-Robin Rieger, Jonas Jentsch, Carsten |
| author_facet | Kolli, Imene Lange, Kai-Robin Rieger, Jonas Jentsch, Carsten |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22410 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking Kolli, Imene Lange, Kai-Robin Rieger, Jonas Jentsch, Carsten Computation and Language 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. |
| title | Word-Centered Semantic Graphs for Interpretable Diachronic Sense Tracking |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.22410 |