Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec. However the technique is different and training is performed on an aggregated global word-word co-occurrence matrix, giving us a vector space with meaningful sub-structures. This method was invented in Stanford by Pennington et al. and I recommend you to read the original paper on GloVe, 'GloVe: Global Vectors for Word Representation' by Pennington et al. which is an excellent read to get some perspective on how this model works. We won't cover the implementation of the model from scratch in too much detail here but if you are interested in the actual code, you can check out the official GloVe page.