用依存共现模式检测语义变化，效果优于主流模型且结果可解释。
Transparent Semantic Change Detection with Dependency-Based Profiles
- 基于词语依存共现构建语义特征，不依赖神经网络嵌入。
- 在多个基准上表现超越分布语义模型，最高提升2.3%准确率。
- 预测结果直观可解释，适合需要透明性的自然语言分析场景。
当前大多数词汇语义变化检测（LSC）方法依赖神经网络的嵌入式分布词表示，尽管在基准测试中表现优异，但通常缺乏透明性。本文提出一种纯依赖于词语依存共现模式的替代方法。实验表明该方法在语义变化检测任务中同样有效，甚至在多个基准上超越了若干分布语义模型。我们进行了深入的定量与定性分析，验证了预测结果的合理性与可解释性。
原文摘要 · Abstract (English)
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.
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