This is interesting. Especially the bit about compounding taking the meaning from figurative to literal. Modern NLP embeddings still assume a canonical meaning

This is interesting. Especially the bit about compounding taking the meaning from figurative to literal. Modern NLP embeddings still assume a canonical meaning for each word and therefore cannot (immediately, anyway) distinguish between a figurative or literal meaning. Not that such efforts don't exist, but they all seem to be missing something fundamental - namely, is the "canonical" meaning, the vector, supposed to be literal, or figurative? This is a question with potentially no right answer, yet we all assume that the answer is "literal". At the same time, I do wonder if part of the problem with NLP algorithms and Finnish isn't so much its complexity but the fact that there id very little Finnish data to train on.