Glove: Global Vectors for Word Representation

@inproceedings{Pennington2014GloveGV,
  title={Glove: Global Vectors for Word Representation},
  author={Jeffrey Pennington and Richard Socher and Christopher D. Manning},
  booktitle={EMNLP},
  year={2014}
}
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and… CONTINUE READING

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Key Quantitative Results

  • The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task.

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