  Recent advances in natural language processing (NLP) have produced general
models that can perform complex tasks such as summarizing long passages and
translating across languages. Here, we introduce a method to extract adjective
similarities from language models as done with survey-based ratings in
traditional psycholexical studies but using millions of times more text in a
natural setting. The correlational structure produced through this method is
highly similar to that of self- and other-ratings of 435 terms reported by
Saucier and Goldberg (1996a). The first three unrotated factors produced using
NLP are congruent with those in survey data, with coefficients of 0.89, 0.79,
and 0.79. This structure is robust to many modeling decisions: adjective set,
including those with 1,710 terms (Goldberg, 1982) and 18,000 terms (Allport &
Odbert, 1936); the query used to extract correlations; and language model.
Notably, Neuroticism and Openness are only weakly and inconsistently recovered.
This is a new source of signal that is closer to the original (semantic) vision
of the Lexical Hypothesis. The method can be applied where surveys cannot: in
dozens of languages simultaneously, with tens of thousands of items, on
historical text, and at extremely large scale for little cost. The code is made
public to facilitate reproduction and fast iteration in new directions of
research.
