Hello, I have checked the mailing list archive for this question but couldn't find anything. I'd be surprised if this question has not been asked yet, if it has, I'd be happy if you could point me to the corresponding mails.
Recently, word representations induced by neural networks have gained a lot of momentum. Particularly often cited in this context is: http://code.google.com/p/word2vec/ Those vector word representations are vectors that carry some semantic meaning in them, i.e. semantically similar words have similar vectors (small distances to each other). I have been wondering about the best way to incorporate them in Moses. One solution would be to incorporate them as factors in a factored model: http://www.statmt.org/moses/?n=Moses.FactoredTutorial It seems to me that I would have to treat each dimension of each word vector as a separate factor which would lead to a lot of factors. Usual dimensionalities of those word vectors are 200 or more. Is treating each dimension as a factor the best way to incorporate those vectors or is there anything better I can do? I don't have to stick to factors, if there is another way. Thank you in advance! Best, Hubert _______________________________________________ Moses-support mailing list [email protected] http://mailman.mit.edu/mailman/listinfo/moses-support
