Thank you, Boris. I am new to DeepLearning, so I have no idea the issues we would face. I was wondering if we can use Features2Vec instead of Word2Vec, does it make any sense? The idea was to use DL in low level NLP tasks where we don't have parse trees yet.
2016-06-29 6:34 GMT-03:00 Boris Galitsky <bgalit...@hotmail.com>: > Hi guys > > I should mention how we used DeepLearning4J for the OpenNLP.Similarity > project at > > https://github.com/bgalitsky/relevance-based-on-parse-trees > > > The main question is how word2vec models and linguistic information such > as part trees complement each other. In a word2vec approach any two words > can be compared. The weakness here is that when learning is based on > computing a distance between totally unrelated words like 'cat' and 'fly' > can be meaningless, uninformative and can corrupt a learning model. > > > In OpenNLP.Similarity component similarity is defined in terms of parse > trees. When word2vec is applied on top of parse trees and not as a > bag-of-words, we only compute the distance between the words with the same > semantic role, so the model becomes more accurate. > > > There's a paper on the way which does the assessment of relevance > improvent for > > > word2vec (bag-of-words) [traditional] vs word2vec (parse-trees) > > > Regards > > Boris > > [https://avatars3.githubusercontent.com/u/1051120?v=3&s=400]< > https://github.com/bgalitsky/relevance-based-on-parse-trees> > > bgalitsky/relevance-based-on-parse-trees< > https://github.com/bgalitsky/relevance-based-on-parse-trees> > github.com > Automatically exported from > code.google.com/p/relevance-based-on-parse-trees > > > > > ________________________________ > From: Anthony Beylerian <anthony.beyler...@gmail.com> > Sent: Wednesday, June 29, 2016 2:13:38 AM > To: dev@opennlp.apache.org > Subject: Re: DeepLearning4J as a ML for OpenNLP > > +1 would be willing to help out when possible >
