Ordinary Meaning in Legal Interpretation: A Proposal from a Corpus (and a Bit of an LLM) Perspective

This paper first surveys a variety of problems endemic in the notion of ordinary meaning and then argues in favor of a semantic approach to ordinary meaning that is intensionalist, prototype-based, and corpus-linguistic in nature. This approach uses embeddings models rather than large language models (LLMs), which makes it more replicable, versatile (it can be applied to words that did not exist at corpus time), and more cheaply/quickly adoptable than the current traditional practice. I exemplify the approach using two embeddings models - one from a 2014 Common Crawl of the WWW, the other trained on 1950s American English corpus data.