Road 2 NLP- Word Embedding词向量（GloVe）

Eajack Lau 6月 22, 2019

参考资料

GloVe开山之作：《GloVe: Global Vectors for Word Representation》，作者Jeffrey Pennington（Stanford NLP）

CSDN博客文章：理解GloVe模型（+总结）

个人觉得这篇文章：理解GloVe模型（+总结），已经讲的很好了…我直接看这文章弄懂了GloVe原理的。有了上一篇Word2vec基础，GloVe原理不是很难：

按照GloVe原文可知，GloVe综合2类Word Representation模型优点：

The two main model families for learning word vectors are: 
1) global matrix factorization methods,such as latent semantic analysis (LSA) (Deerwester et al., 1990) and 
2) local context window methods, such as the skip-gram model of Mikolov et al. (2013c).

GloVe = global matrix factorization methods + local context window methods