arXiv Open Access 2026

ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics

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

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Abstrak

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

Topik & Kata Kunci

cs.CL

Penulis (5)

Bach Phan-Tat

Kris Heylen

Dirk Geeraerts

Stefano De Pascale

Dirk Speelman

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APA MLA BibTeX

Phan-Tat, B., Heylen, K., Geeraerts, D., Pascale, S.D., Speelman, D. (2026). ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics. https://arxiv.org/abs/2602.04514

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Tahun Terbit: 2026
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓