A Computational Evaluation of Two Laws of Semantic Change
For more than a century scholars have proposed laws of se-\nmantic change that characterize how words change in meaning\nover time. Two such laws are the law of differentiation, which\nproposes that near-synonyms tend to differentiate in meaning\nover time, and the law of parallel change, which proposes that\nrelated words tend to undergo parallel changes in meaning. Re-\nsearchers have identified a handful of changes that are consis-\ntent with each proposed law, but there are no systematic eval-\nuations that assess the validity and generality of these compet-\ning laws. Here we evaluate these laws by using a large corpus\nto assess how thousands of related words changed in meaning\nover the twentieth century. Our analyses show that the law of\nparallel change applies more broadly than the law of differ-\nentiation, and thereby illustrate how large-scale computational\nanalyses can place laws of semantic change on a more secure\nfooting.
For more than a century scholars have proposed laws of se-\nmantic change that characterize how words change in meaning\nover time. Two such laws are the law of differentiation, which\nproposes that near-synonyms tend to differentiate in meaning\nover time, and the law of parallel change, which proposes that\nrelated words tend to undergo parallel changes in meaning. Re-\nsearchers have identified a handful of changes that are consis-\ntent with each proposed law, but there are no systematic eval-\nuations that assess the validity and generality of these compet-\ning laws. Here we evaluate these laws by using a large corpus\nto assess how thousands of related words changed in meaning\nover the twentieth century. Our analyses show that the law of\nparallel change applies more broadly than the law of differ-\nentiation, and thereby illustrate how large-scale computational\nanalyses can place laws of semantic change on a more secure\nfooting.
