Transparent Semantic Change Detection with Dependency-Based Profiles

• URL: http://arxiv.org/abs/2601.02891v1
• Date: Tue, 06 Jan 2026 10:25:36 GMT
• Title: Transparent Semantic Change Detection with Dependency-Based Profiles
• Authors: Bach Phan-Tat, Kris Heylen, Dirk Geeraerts, Stefano De Pascale, Dirk Speelman,
• Abstract summary: We investigate an alternative method which relies purely on dependency co-occurrence patterns of words.<n>We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models.
• Score: 1.1340133299604382
• License: http://creativecommons.org/licenses/by/4.0/
• 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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