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176 points nxa | 2 comments | | HN request time: 0s | 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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tiborsaas ◴[] No.43993288[source]
I've tried to get to "garage", but failed at a few attempts, ChatGPT's ideas also seemed reasonable, but failed. Any takers? :)
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1. mynameajeff ◴[] No.43996633[source]
"car + house + door" worked for me (interestingly "car + home + door" did not)
replies(1): >>44000933 #
2. tiborsaas ◴[] No.44000933[source]
Thanks, nice :) House sounds more general, I guess.

I've had some fun finding this:

    car - move + shape = car wheel