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176 points nxa | 1 comments | | HN request time: 0.205s | 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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ericdiao ◴[] No.43989254[source]
Interesting: parent + male = female (83%)

Can not personally find the connection here, was expecting father or something.

replies(1): >>43989271 #
1. ericdiao ◴[] No.43989271[source]
Though dad is in the list with lower confidence (77%).

High dimension vector is always hard to explain. This is an example.