Cultural influences on word meanings revealed through large-scale semantic alignment

Thompson, B., Roberts, S. G., & Lupyan, G. (2020). Cultural influences on word meanings revealed through large-scale semantic alignment. Nature Human Behaviour. doi:10.1038/s41562-020-0924-8

Abstract: If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. By contrast, if shared word meanings are a product of shared culture, history and geography, they may differ between languages in substantial but predictable ways. Here, we analysed the semantic neighbourhoods of 1,010 meanings in 41 languages. The most-aligned words were from semantic domains with high internal structure (number, quantity and kinship). Words denoting natural kinds, common actions and artefacts aligned much less well. Languages that are more geographically proximate, more historically related and/or spoken by more-similar cultures had more aligned word meanings. These results provide evidence that the meanings of common words vary in ways that reflect the culture, history and geography of their users.

Rache Nuwer,  Machine learning reveals role of culture in shaping meanings of words, Phys.org, August 17, 2020:

Instead of attempting to define the words, the large-scale method uses the concept of "semantic associations," or simply words that have a meaningful relationship to each other, which linguists find to be one of the best ways to go about defining a word and comparing it to another. Semantic associates of "beautiful," for example, include "colorful," "love," "precious" and "delicate."

The researchers built an algorithm that examined neural networks trained on various languages to compare millions of semantic associations. The algorithm translated the semantic associates of a particular word into another language, and then repeated the process the other way around. For example, the algorithm translated the semantic associates of "beautiful" into French and then translated the semantic associates of beau into English. The algorithm's final similarity score for a word's meaning came from quantifying how closely the semantics aligned in both directions of the translation.

And now, culture:

In one final step, the researchers applied another algorithm that compared how similar the cultures that produced the two languages are, based on an anthropological dataset comparing things like marriage practices, legal systems and political organization of given language's speakers.

The researchers found that their algorithm could correctly predict how easily two languages could be translated based on how similar the two cultures that speak them are. This shows that variability in word meaning is not just random. Culture plays a strong role in shaping languages—a hypothesis that theory has long predicted, but that researchers lacked quantitative data to support.