GloVe (machine learning)

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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] 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]

Applications[edit]

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.[4] It was also used as the word representation framework for the online and offline systems designed to detect psychological distress in patient interviews.[1]

History[edit]

It was launched in 2014.

See also[edit]

References[edit]

  1. ^ a b 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.
  2. ^ 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."
  3. ^ Kalajdziski, Slobodan (2018). ICT Innovations 2018. Engineering and Life Sciences. Cham: Springer. p. 220. ISBN 9783030008246.
  4. ^ Singh, Mayank; Gupta, P. K.; Tyagi, Vipin; Flusser, Jan; Ören, Tuncer I. (2018). Advances in Computing and Data Sciences: Second International Conference, ICACDS 2018, Dehradun, India, April 20-21, 2018, Revised Selected Papers. Singapore: Springer. p. 171. ISBN 9789811318122.

External links[edit]