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

翻译：当前大多数词汇语义变化检测的计算方法均基于神经嵌入分布表示。尽管这些模型在LSC基准测试中表现良好，但其结果往往难以解释。我们探索了一种仅依赖框架语义的替代方法。研究表明，该方法能有效检测语义变化，其性能甚至可超越许多分布语义模型。最后，我们对其预测结果进行了详细的定量与定性分析，证明这些预测既合理又具有高度可解释性。