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176 points nxa | 1 comments | | HN request time: 0.266s | 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.

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hagen_dogs ◴[] No.43991701[source]
fluid + liquid = solid (85%) -- didn't expect that

blue + red = yellow (87%) -- rgb, neat

black + {red,blue,yellow,green} = white 83% -- weird

replies(1): >>43992563 #
1. moefh ◴[] No.43992563[source]
> blue + red = yellow (87%) -- rgb, neat

Blue + red is magenta. Yellow would be red + green.

None of these results make much sense to me.