GloVe, short for Global Vectors, is an unsupervised learning model used to obtain dense vector representations for words. It captures global corpus statistics by modeling the ratios of probabilities from a global word-word co-occurrence matrix. By doing so, it incorporates both the advantages of global matrix factorization and the local context window methods (like word2vec) to effectively represent the linear substructures of the word vector space.