Word2Vec (Mikolov et al., 2013) uses shallow neural networks with negative sampling techniques, which are trained to predict either the word from the context or the context from the word using a fixed window of words as the context.
we choose the CBOW version (predict the word using the context) of this model which was trained using the gensim package with the minimum word count threshold set to 0 (i.e., a vector representation was created for all words in the corpus).