Word2Vec for Named Entity Recognition I'm looking to use google's word2vec implementation to build a named entity recognition system.  I've heard that recursive neural nets with back propagation through structure are well suited for named entity recognition tasks, but I've been unable to find a decent implementation or a decent tutorial for that type of model. Because I'm working with an atypical corpus, standard NER tools in NLTK and similar have performed very poorly, and it looks like I'll have to train my own system.   

In short, what resources are available for this kind of problem?  Is there a standard recursive neural net implementation available?
Two recent papers use a Deep learning architecture called CharWNN to address this problem. CharWNN was first used to get state of the art results (without handcrafted features) on Part of Speech (POS) tagging on an English corpus.

The second paper by the same author uses the same (or similar) architecture for predicting whether a word belongs to 10 Named Entity classes, with apparent state of the art results.