用框架语义学检测词汇语义变化，效果好且解释性强
ReFRAME or Remain: Unsupervised Lexical Semantic Change Detection with Frame Semantics
- 仅依赖框架语义分析，不使用神经嵌入
- 在多个数据集上优于部分分布语义模型
- 结果可解释性强，适合需要透明性的场景
当前大多数词汇语义变化检测方法基于神经嵌入的分布表示。尽管这些模型在标准基准上表现良好，但结果往往难以解释。本文探索一种完全基于框架语义的方法，证明其在检测语义变化方面有效，甚至超越多种分布语义模型。最后，我们通过定量与定性分析验证了预测结果的合理性和高度可解释性。
原文摘要 · Abstract (English)
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
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