印欧语系语言中的亚扩散语义演化
Subdiffusive semantic evolution in Indo-European languages
AI总结：
本文利用历时分布语义嵌入方法，发现五种主要印欧语言中词汇语义演化普遍遵循亚扩散规律，反常扩散指数约为0.45，并揭示了变化方向时间相关性对产生该现象的关键作用。
摘要：
词汇如何改变其含义？尽管语义演化受到多种不同因素的驱动，包括语言学、社会和技术因素，但我们发现，在五种主要印欧语言中普遍存在一条定律：语义演化具有强烈的亚扩散性。我们使用一种自动化的历时分布语义嵌入流程，并控制潜在的对称性，结果表明，词汇在语义空间中遵循随机轨迹，其反常扩散指数为 $α= 0.45\pm 0.05$，且这一结果在不同语言中一致，而扩散粒子的指数为 $α=1$。随机化方法表明，保留语义变化方向上的时间相关性对于重现强烈的亚扩散行为是必要的；然而，变化幅度上的相关性也起着重要作用。我们进一步表明，在数据分析和解释的多种不同选择下，例如选择拟合位移的系综平均值或对单个词汇轨迹的最佳拟合指数取平均，强亚扩散都是一个稳健的现象。
英文摘要：
How do words change their meaning? Although semantic evolution is driven by a variety of distinct factors, including linguistic, societal, and technological ones, we find that there is one law that holds universally across five major Indo-European languages: that semantic evolution is strongly subdiffusive. Using an automated pipeline of diachronic distributional semantic embedding that controls for underlying symmetries, we show that words follow stochastic trajectories in meaning space with an anomalous diffusion exponent $α= 0.45\pm 0.05$ across languages, in contrast with diffusing particles that follow $α=1$. Randomization methods indicate that preserving temporal correlations in semantic change directions is necessary to recover strongly subdiffusive behavior; however, correlations in change sizes play an important role too. We furthermore show that strong subdiffusion is a robust phenomenon under a wide variety of choices in data analysis and interpretation, such as the choice of fitting an ensemble average of displacements or averaging best-fit exponents of individual word trajectories.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
