https://www.scientificamerican.com/article/how-dozens-of-languages-help-build-gender-stereotypes/

Linguists use machine-learning techniques for mining large text corpora to detect how the structure of a language lends meaning to its words. They work on the assumption that terms that appear in close proximity to one another may have similar connotations: dogs turn up near cats more often than canines appear close to bananas.

This same method of burrowing into texts—more formally called the search for distributional semantics—can also provide a framework for analyzing psychological attitudes, including gender stereotypes that contribute to the underrepresentation of women in scientific and technical fields. Studies in English have shown, for example, that the word “woman” often appears close to “home” and “family,” whereas “man” is frequently paired with “job” and “money.”