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176 points nxa | 1 comments | | HN request time: 0.202s | source

I've been playing with embeddings and wanted to try out what results the embedding layer will produce based on just word-by-word input and addition / subtraction, beyond what many videos / papers mention (like the obvious king-man+woman=queen). So I built something that doesn't just give the first answer, but ranks the matches based on distance / cosine symmetry. I polished it a bit so that others can try it out, too.

For now, I only have nouns (and some proper nouns) in the dataset, and pick the most common interpretation among the homographs. Also, it's case sensitive.

1. rdlw ◴[] No.43990084[source]
I've always wondered if there's s way to find which vectors are most important in a model like this. The gender vector man-woman or woman-man is the one always used in examples, since English has many gendered terms, but I wonder if it's possible to generate these pairs given the data. Maybe to list all differences of pairs of vectors, and see if there are any clusters. I imagine some grammatical features would show up, like the plurality vector people-person, or the past tense vector walked-walk, but maybe there would be some that are surprisingly common but don't seem to map cleanly to an obvious concept.

Or maybe they would all be completely inscrutable and man-woman would be like the 50th strongest result.