Word2vec is a pervasive tool for learning word embeddings. Its success, however, is mostly due to particular architecture choices. Transferring these choices to traditional distributional methods makes them competitive with popular word embedding methods. 24 Sep 2016 • 10 min read This post will discuss the factors that account for the success of word2vec and its connection to more traditional models. Table of Contents: GloVe Word embeddings vs. distributional semantic models Models Hyperparamet...