GloVe: Global Vectors for Word Representation

作者： Jeffrey Pennington, Richard Socher, Christopher D. Manning (2014)

领域

架构

TLDR（中文）

GloVe 通过分解词共现矩阵来学习词向量，结合了基于计数的方法（LSA）和基于预测的方法 （Word2Vec）的优点。在词类比和词相似度任务上达到了当时最先进的性能， 是学术界广泛使用的基线词向量。

TLDR (English)

GloVe learns word vectors by factorizing word co-occurrence matrices, combining the advantages of count-based methods (LSA) and prediction-based methods (Word2Vec). It achieved state-of-the-art on word analogy and similarity tasks and remains a widely used baseline word vector in academia.

出现在这些文章里

• Embeddings：把离散符号放进连续空间
• Embeddings: Putting Discrete Symbols into Continuous Space

同被引用

这些论文与本文出现在同一篇文章中

• Efficient Estimation of Word Representations in Vector Space Tomas Mikolov et al. (2013)
• Deep contextualized word representations Matthew E. Peters et al. (2018)

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