Word2vec is usually the standard neural word embeddings implementation. There are other algorithms as well such as glove[1], document embeddings[2] and backprop

Word2vec is usually the standard neural word embeddings implementation. There are other algorithms as well such as glove[1], document embeddings[2] and backpropagation based methods[3]. Facebook just came out with a paper recently that beat word2vec as well[4]. Neural word embeddings are a neat way of representing concepts. I see a great future for automated feature engineering with text (joining audio and images) in deep learning. [1]: http://nlp.stanford.edu/pubs/glove.pdf http://nlp.stanford.edu/pubs/glove.pdf [2]: http://cs.stanford.edu/~quocle/paragraph_vector.pdf http://cs.stanford.edu/~quocle/paragraph_vector.pdf [3]:http://www.australianscience.com.au/research/google/35671.pdf http://www.australianscience.com.au/research/google/35671.pd... [4]: http://arxiv.org/abs/1502.01710 http://arxiv.org/abs/1502.01710