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arXiv · 2601.02891

Transparent Semantic Change Detection with Dependency-Based Profiles

Bach Phan-Tat·Kris Heylen·Dirk Geeraerts·Stefano De Pascale·Dirk Speelman
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Abstract

Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable.

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Bach Phan-Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, Dirk Speelman. 2026-01-06. Transparent Semantic Change Detection with Dependency-Based Profiles. https://doi.org/10.18653/v1%2F2026.lchange-1.8

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