ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics
This work addresses the interpretability issue in lexical semantic change detection for computational linguistics, offering a novel alternative to neural embedding methods.
The paper tackled the problem of lexical semantic change detection by proposing an unsupervised method based on frame semantics, which outperformed many distributional semantic models and provided interpretable results.
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