I'm not associated with the OP, but I have played with word2vec. Word2vec can be viewed as dimensionality reduction. In some sense, the dimensionality of words

I'm not associated with the OP, but I have played with word2vec. Word2vec can be viewed as dimensionality reduction. In some sense, the dimensionality of words is the same as the vocabulary size, if a one-hot encoding is used (which is most common). For word2vec specifically, the output vector dimension is a parameter specified during training, so you can choose it however you want. I suspect the OP chose something fairly large (>256) because of the performance boost. Nearest neighbor methods work well with word2vec words: https://code.google.com/p/word2vec/#Word_clustering https://code.google.com/p/word2vec/#Word_clustering The paper associated with word2vec is fairly easy to read and understand, even if you don't have a background in NLP or neural networks: http://arxiv.org/pdf/1301.3781.pdf http://arxiv.org/pdf/1301.3781.pdf