Transparent Semantic Change Detection with Dependency-Based Profiles
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 61 references that are in the map. First posted on arXiv 6 Jan 2026.
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20 of 61 references.
References · 20
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8 more
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Citing papers per year: 2026: 3.
3 papers.