Corpus Linguistics, LLM AIs, And the Assessment of Ordinary Meaning
More and more, judges are seeing the assessment of ordinary meaning as an empirical matter—an inquiry into the way legal words or phrases are commonly used by the public. This inquiry is viewed as furthering some core tenets of textualism. It views the assessment of the ordinary meaning of words as transparent, determinate, and constraining—much more so than a free-wheeling inquiry into the intent of the legislative body.
For many years and for many interpretive questions, dictionaries were viewed as the gold standard. To resolve an interpretive question, all the judge had to do was declare that the ordinary meaning of the text controls, note that dictionaries are reliable evidence of such meaning, and cite a dictionary definition as the decisive basis for decision.
Over time, both scholars and judges have come to question the viability of that approach—especially in cases where competing dictionary definitions provide support for both sides of a case. In that event, a judge’s intuitive preference for one definition over another isn’t transparent. And it isn’t any more constraining than a subjective assessment of legislative intent.
That does not mean that the ordinary meaning inquiry is lost. It just means that we need more sophisticated tools to answer it.
Increasingly, scholars and judges are acknowledging that the empirical dimensions of the ordinary meaning inquiry call for data. And they are turning to tools aimed at producing transparent, replicable evidence of how the language of law is commonly or typically used by the public. A key set of those tools come from the field of corpus linguistics—a field that studies language use by examining large databases (corpora) of naturally occurring language.
When properly applied, corpus linguistic analysis delivers on the promises of empirical textualism. A judge who is interested in assessing how a term is commonly used by the public can perform a replicable search of that term in a corpus designed to represent language use by the relevant speech community. Such a search will yield
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