There is an interesting concept in machine learning called "word vectors" or word2vec. They measure how often words occur near each other, and then try to reduc

There is an interesting concept in machine learning called "word vectors" or word2vec. They measure how often words occur near each other, and then try to reduce it to a vector that carries most of that information. This results in similar words having similar vectors. And even a very interesting result that you can do math on them. E.g. "king"-"man"+"woman" gives a word vector very close to "queen". I imagine this is at least somewhat similar to how words are represented in the human brain. I wonder if you looked at the space of all word vectors, and found areas where words were missing. Do to some quirk of language, there was no word to represent that point in concept space. You could also compared the vector space for multiple different languages, and automatically determine what words don't have equivalents.