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
