Transfer-learning is one possible approach :

 1. Design and implment a neural net to match Google Word2Vec's design
 (In terms of number of layers, activation functions etc). https://github.com/dav/word2vec/blob/master/src/word2vec.c
 2. Pre-initialize weights with these vectors 
 3. Retrain with domain-specific corpus 

This is an implementation that can be used as base and modified for step #1 https://github.com/tensorflow/models/blob/master/tutorials/embedding/word2vec.py