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。尽管这些方法在语义变化检测基准测试中表现出色，但其过程往往缺乏透明度。本研究探索了一种仅依赖词语依存共现模式的替代方法。实验证明，该方法在语义变化检测任务中具有良好效果，其性能甚至优于多种分布式语义模型。我们通过深入的定量与定性分析表明，该方法的预测结果不仅合理，且具有可解释性。