Algorithm for obtaining vector representations of words
Not to be confused with Glove.
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.[1] 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[2] and was launched in 2014. 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.[3]
^Abad, Alberto; Ortega, Alfonso; Teixeira, António; Mateo, Carmen; Hinarejos, Carlos; Perdigão, Fernando; Batista, Fernando; Mamede, Nuno (2016). Advances in Speech and Language Technologies for Iberian Languages: Third International Conference, IberSPEECH 2016, Lisbon, Portugal, November 23-25, 2016, Proceedings. Cham: Springer. p. 165. ISBN 9783319491691.
^GloVe: Global Vectors for Word Representation (pdf) "We use our insights to construct a new model for word representation which we call GloVe, for Global Vectors, because the global corpus statistics are captured directly by the model."
^Kalajdziski, Slobodan (2018). ICT Innovations 2018. Engineering and Life Sciences. Cham: Springer. p. 220. ISBN 9783030008246.
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