Here is what he's publishing: http://arxiv.org/find/cs/1/au:+Hinton_G/0/1/0/all/0/1
This seems to be a good introduction to the topic: http://arxiv.org/pdf/1310.4546v1.pdf
This is about paragraph vectors: http://arxiv.org/abs/1507.07998 http://arxiv.org/abs/1405.4053
The first paragraph paper vector (Le and Mikolov 2014) was irreproducible [1]. Not even Mikolov could reproduce it.
Paragraph vectors also fundamentally involve training on your test set: the paper stresses the importance of all the vectors being learned jointly, without addressing why this is a problem for evaluation.
The developers of gensim have been making an effort to make a version of doc2vec that can be applied to documents it was not trained on (of course it doesn't perform as well). They seem content to clean up after Google's messy publications, but in a fair world, they would be the ones getting the citations if they succeed at this.
[1] http://stats.stackexchange.com/questions/123562/has-the-repo...
The lawyers have since stepped up their game IMO, doing their best to protect us from that imminent Robot Apocalypse Elon Musk keeps warning us is right around the corner.
I assumed the worst when I read your comment; I figured that neural net semantics was going to stall for 17 years the same way that basic morphology did when Xerox patented FSTs. But it looks like word2vec is Apache-licensed, meaning the patent can only be used defensively. Phew.