Word2vec è un modello (evoluzione Doc2vec) https://sourcedexter.com/tensorflow-text-classification/ https://github.com/tflearn/tflearn Vector space models (VSMs) represent (embed) words in a continuous vector space where semantically similar words are mapped to nearby points ('are embedded nearby each other') Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Section 3.1 and 3.2 in Mikolov et al. ) Skip Gram Model http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ Bag-Of-Words Model https://ongspxm.github.io/blog/2014/12/bag-of-words-natural-language-processing/ Il modello è semplice perchè usa un trucco del auto-encoder l'output layer fa deve ritornare la stessa cosa dell'input (apprendimento supervisionato anche se non si hanno le label) poi si rimuove il livello di output e si ottengono i p...
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