ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics
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Abstract (arXiv)
The majority of contemporary computational methods for lexical semantic change (LSC) detection are based on neural embedding distributional representations. Although these models perform well on LSC benchmarks, their results are often difficult to interpret. We explore an alternative approach that relies solely on frame semantics. We show that this method is effective for detecting semantic change and can even outperform many distributional semantic models. Finally, we present a detailed quantitative and qualitative analysis of its predictions, demonstrating that they are both plausible and highly interpretable
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 92 references that are in the map. First posted on arXiv 4 Feb 2026.
Builds on
23 of 92 references.
References · 23
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Citing papers per year: 2026: 2.
2 papers.