Most modern computational approaches to lexical semantic change detection (LSC) rely on embedding-based distributional word representations with neural networks. Despite the strong performance on LSC benchmarks, they are often opaque. We investigate an alternative method which relies purely on dependency co-occurrence patterns of words. We demonstrate that it is effective for semantic change detection and even outperforms a number of distributional semantic models. We provide an in-depth quantitative and qualitative analysis of the predictions, showing that they are plausible and interpretable.

翻译：当前大多数词汇语义变化检测的计算方法都依赖于基于嵌入的分布式词表示与神经网络。尽管在语义变化检测基准测试中表现出色，这些方法通常缺乏可解释性。本研究探讨了一种完全基于词汇依存共现模式的替代方法。我们证明该方法在语义变化检测中具有有效性，甚至优于多种分布式语义模型。通过对预测结果进行深入的定量与定性分析，我们表明该方法的预测结果具有合理性与可解释性。