论文标题
印欧语语言中的延伸语义演变
Subdiffusive semantic evolution in Indo-European languages
论文作者
论文摘要
单词如何改变他们的含义？尽管语义演化是由多种不同的因素（包括语言，社会和技术方面的）驱动的，但我们发现有一项法律在五种主要的印度 - 欧洲语言中普遍存在：这种语义进化是强烈的延伸。使用控制基础对称性的简介分布语义嵌入的自动管道，我们表明单词遵循含义空间中随机轨迹，具有异常扩散指数$α= 0.45 \ pm 0.05 \ pm 0.05 $ 0.05 $ 0.05 $跨语言，相比之下，与延伸粒子相比，遵循$α= 1 $的扩散粒子。随机化方法表明，在语义变化方向上保留时间相关性是为了恢复强烈的延伸行为所必需的。但是，变化大小的相关性也起着重要作用。我们此外表明，在数据分析和解释中，强大的次扩散是一种强大的现象，例如选择拟合位移平均值或平均单个单词轨迹的最佳指数的选择。
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.
