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It seems obvious to me that disambiguating _correctly_ would necessarily improve performance. As an extreme example, homonyms like bark (the verb) and bark (on a tree) have nothing to do with each other, and ideally would be considered two different words.


I imagine contranyms would be among the most difficult here. Would be fun to see how these vectors stack up with those.


Well, antonyms are usually right next to each other in word embedding spaces anyways. (since the context they're used in is often hard to tell apart)


Contranyms would be identical in the embedding space, no?

I'm actually intrigued that nobody has made a text processor named after https://en.wikipedia.org/wiki/Amelia_Bedelia yet. :)


Yeah I would agree. I've struggled to find much work that looks at this at a large scale though!


AllenNLP’s recent work ELMo is very good at this task. Check it out!


Much appreciated! Will have a look.




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