4 ms·
> Another problem of word vectors is that any word might actually have multiple senses, while vectors are just point estimates. If we wanted to be correct, we n
by aglionby 9y ago
> Another problem of word vectors is that any word might actually have multiple senses, while vectors are just point estimates. If we wanted to be correct, we needed first to find the right sense for each word in a phrase and only then assign the vector. There is research in "on-the-fly" word vectors that adapt to context, but it's much harder to use.
This is interesting, and there seems to be a bit of debate about it (at least with compositional distributional semantic models). [0][1] seem to show that sense disambiguation helps in some contexts, and [2] show that they don't in others. It doesn't seem immediately clear who is right here. I agree with you though that it seems pretty likely that disambiguating would be helpful.
[0] https://www.aclweb.org/anthology/W13-3513 https://www.aclweb.org/anthology/W13-3513
[1] https://www.aclweb.org/anthology/P16-1018 https://www.aclweb.org/anthology/P16-1018
[2] https://www.aclweb.org/anthology/D10-1115 https://www.aclweb.org/anthology/D10-1115
- kevinwang 9y agoIt 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.
- aglionby 9y agoYeah I would agree. I've struggled to find much work that looks at this at a large scale though!
- nicklovescode 9y agoAllenNLP’s recent work ELMo is very good at this task. Check it out!
- aglionby 9y agoMuch appreciated! Will have a look.
- taeric 9y agoI imagine contranyms would be among the most difficult here. Would be fun to see how these vectors stack up with those.
- kevinwang 9y agoWell, 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)
- taeric 9y agoContranyms 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 https://en.wikipedia.org/wiki/Amelia_Bedelia yet. :)
- nl 9y agoActually... This seems like it should obviously make sense, but in practice it doesn’t necessarily. Usually the sense in defined by the context, and so the vector can carry the meaning of multiple contexts at once. There are some specific tasks where this breaks for some words. I can’t remember one I’ve seen right now, but thinking about the “bark” example, it can cause a problem when both the “woodyness” and “noisyness” of a word cause completely different results. In practice that’s pretty rare. Generally though, the non-relevant meanings don’t have any negative effects as they can be ignored.