  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.
