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.
King-man+woman=Navratilova, who is apparently a Czech tennis player. Apparently, it's very case-sensitive. Cool idea!
doctor - man + woman = medical practitioner
Good to understand this bias before blindly applying these models (Yes- doctor is gender neutral - even women can be doctors!!)
Woman + president = man
male + age = female
female + age = male
horse+man
78% male horse 72% horseman
dog+woman = man
That's weird.
dog - fur = Aegean civilization (22%)
huh
rice + fish = fish meat
rice + fish + raw = meat
hahaha... I JUST WANT SUSHI!
man + woman = adult female body
it doesn't know the word human
twelve-ten+five=
six (84%)
Close enough I suppose
I'm getting Navralitova instead of queen. And can't get other words to work, I get red circles or no answer at all.
The app produces nonsense ... such as quantum - superposition = quantum theory !!!
garden + sin = gardening
hmm...
colorless+green+ideas doesn't produce anything of interest, which is disappointing.
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Can someone explain me what the fuck this is supposed to be!?
cheeseburger-giraffe+space-kidney-monkey = cheesecake
king - man + woman = queen
queen - woman + man = drone