GloVe (machine learning)
GloVe, coined from Global Vectors, is a model for distributed word representation. The model is an unsupervised learning algorithm for obtaining vector representations for words. This is achieved by mapping words into a meaningful space where the distance between words is related to semantic similarity. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. It is developed as an open-source project at Stanford. As log-bilinear regression model for unsupervised learning of word representations, it combines the features of two model families, namely the global matrix factorization and local context window methods.Applications
GloVe can be used to find relations between words like synonyms, company-product relations, zip codes and cities, etc. It is also used by the SpaCy model to build semantic word embeddings/feature vectors while computing the top list words that match with distance measures such as Cosine Similarity and Euclidean distance approach. It was also used as the word representation framework for the online and offline systems designed to detect psychological distress in patient interviews.History
It was launched in 2014.
