[bglazer]

They didn't run word2vec. They built a LSTM-RNN (Long Short Term Memory Recursive Neural Network). They mention this in the caption of the image showing word clusters.

word2vec and LSTM-RNN both produce word embeddings, which are vector representations of words. They then applied t-SNE, which is a dimensionality reduction technique designed to produce nicely separated 2 dimensional clusters from any high dimensional data. It can do this for any "type" of vector, not just word embeddings.

So, word2vec and LSTM-RNN both make high dimensional vectors out of words. t-SNE takes high dimensional vectors and makes them 2 dimensional.