I am working on a NLP related task. I have about 150 documents, each few pages long (5/6 pages long on average). After removing stopwords and other unnecessary symbols and digits, I have about 104,000 unique words. The task at hand probably require some kind of word embedding (such as word2vec) as simple bag-of-words type approach aren't working properly. However, I am concerned about the size of the data I have. I have looked at pre-trained word embedding (GloVec), however, due to the narrow focus of the domain (manufacturing) of our texts, I am hesitating to use these pre-trained vectors. That leaves me with training our own. However, the size of our data set concerns me. Hence I am just throwing this question out there: What should be the lower bound on the size of the vocabulary that we need in order to train a word embedding model (word2vec) that will be reasonable.
Any response would be greatly appreciated.