Lexical Semantic Change is the study of how the meaning of words evolves through time. Another related question is whether and how lexical relations over pairs of words, such as synonymy, change over time. There are currently two competing, apparently opposite hypotheses in the historical linguistic literature regarding how synonymous words evolve: the Law of Differentiation (LD) argues that synonyms tend to take on different meanings over time, whereas the Law of Parallel Change (LPC) claims that synonyms tend to undergo the same semantic change and therefore remain synonyms. So far, there has been little research using distributional models to assess to what extent these laws apply on historical corpora. In this work, we take a first step toward detecting whether LD or LPC operates for given word pairs. After recasting the problem into a more tractable task, we combine two linguistic resources to propose the first complete evaluation framework on this problem and provide empirical evidence in favor of a dominance of LD. We then propose various computational approaches to the problem using Distributional Semantic Models and grounded in recent literature on Lexical Semantic Change detection. Our best approaches achieve a balanced accuracy above 0.6 on our dataset. We discuss challenges still faced by these approaches, such as polysemy or the potential confusion between synonymy and hypernymy.

翻译：词汇语义变化研究的是词义随时间演变的规律。另一个相关问题是词对间的词汇关系（如同义关系）是否及如何随时间变化。关于同义词的演变方式，历史语言学文献中目前存在两种相互竞争且看似对立的理论：分化法则（LD）认为同义词倾向于随时间获得不同含义，而平行变化法则（LPC）则主张同义词会经历相同的语义变化，从而保持同义关系。迄今为止，鲜有研究利用分布语义模型评估这些法则在历史语料库中的适用程度。本文首次尝试检测给定词对是否遵循LD或LPC法则。我们将该问题重构为更易处理的任务后，结合两种语言资源构建了首个完整的评估框架，为LD的主导地位提供了实证依据。随后基于分布语义模型及近期词汇语义变化检测文献，提出多种计算方法。最优方法在数据集上实现了超过0.6的平衡准确率。我们讨论了这些方法仍面临的挑战，例如多义词现象及同义与上下义关系的潜在混淆。