adamklec

This work was done in Python using Theano and Keras.

I agree that's an interesting comparison to make but I'm not sure of the answer. The original purpose of this work was not to generate word vectors but rather to evaluate whether we have enough data to start using deep learning algorithms. That an RNN trained on our data was able to learn word vectors with a significant amount of structure seems like a positive sign. But I don't know how the quality of these word vectors would compare to vectors generated by more standard word2vec algorithms.

That's right. The weights that connect the Nth neuron in the one-hot input layer to the embedding layer can be thought of as a vector encoding of the Nth word in the vocabulary.

Hello. This is Adam. I trained the model and made the visualization. Thanks for your comments.

This model is not a POS tagger. The model was trained to predict the next word in the email given the preceding words. So in that sense, it's similar to the word2vec models discussed in the link you shared. However for this work I used a recurrent neural network to learn a language model of the emails in our database.

After training, I extracted the learned word vectors from the model (they are the weights that connect the input layer which uses a one-hot-encoding of vocab words to the embedding layer). I then used the t-SNE algorithm to reduce the dimensionality of the learned word vectors and then plotted them in 2 dimensions. The colors representing the parts of speech were added after the fact to show that the model had learned to distinguish between nouns, verbs, etc.