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

arXiv (Cornell University)

Abstract

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

Authors 5

1. Bach Phan-Tat

   KU Leuven

   Affiliation as printed

   Department of Linguistics , KU Leuven

2. Kris Heylen Aachen Instituut voor de Nederlandse Taal

   Instituut voor de Nederlandse Taal · KU Leuven

   Affiliation as printed

   Department of Linguistics , KU Leuven

   Instituut voor de Nederlandse Taal

3. Dirk Geeraerts

   KU Leuven

   Affiliation as printed

   Department of Linguistics , KU Leuven

4. Stefano De Pascale

   Vrije Universiteit Brussel · KU Leuven

   Affiliation as printed

   Department of Linguistics , KU Leuven

   Vrije Universiteit Brussel

5. Dirk Speelman

   KU Leuven

   Affiliation as printed

   Department of Linguistics , KU Leuven