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word2vec document vector

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Using Word2Vec document vectors as features in Naive Bayes • /r/MachineLearning

@machinelearnbotApr-28-2016, 11:41:30 GMT

You could learn a discretization, or codebook, of your word2vec features. For example, you could run k-means on all of them (well, all your training word2vec features), then treat each one as a single instance of one of k words. Naive bayes proceeds naturally from documents as histograms of these words, and you don't even have to normalize the word counts. But yeah, it's adding another step, and another parameter (k), and discretization can throw away specificity.

artificial intelligence, machine learning, word2vec document vector, (2 more...)

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