Hands-On Transfer Learning with Python

GloVe model

The GloVe model stands for Global Vectors, which is an unsupervised learning model that 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 substructures. This method was published in the paper, GloVe: Global Vectors for Word Representation by Pennington and their co-authors (https://www.aclweb.org/anthology/D14-1162). We have talked about count-based matrix factorization methods, such as latent semantic analysis (LSA) and predictive methods like Word2vec. This paper claims that currently both families suffer significant drawbacks. Methods like LSA efficiently ...