Sence2vec word embeddings model works better than word2vec , since it utilises contextual information from words.
This reddit vectors dataset is used for training sence2vec model.

The idea behind sense2vec is super simple. If the problem is that duck as in waterfowl and duck as in crouch are different concepts, the straight-forward solution is to just have two entries, duckN and duckV. Trask et al (2015) published a nice set of experiments showing that the idea worked well.

It assigns parts of speech tags like verb, noun , adjective to words, which will in turn be used to make sence of context.

1. Please book [VERB] my ticket.

2. Read the book [NOUN].

Read more here and here

Content

It's corpus of Reddit vectors from reddit comments. (trained on all comments of 2015)

Acknowledgements

This has been released with SpaCy library which is the product of Explosion.ai company.
I have downloaded dataset from below url.
https://github.com/explosion/sense2vec/releases