GloVe is a word embedding model that constructs word vectors based on global co-occurrence statistics. Unlike Word2Vec, which relies on local context windows, GloVe captures the overall statistical relationships between words through matrix factorization. This approach enables GloVe to generate high-quality word representations that effectively encode semantic and syntactic relationships. This article will introduce the principles and training methods of GloVe.

Word2Vec is a model for learning word embeddings, which converts words and their semantics into vectors through neural networks. Word2Vec provides two training methods: CBOW and Skip-gram, and improves efficiency through Negative Sampling and Subsampling technologies. This article will introduce the basic principles and training methods of Word2Vec.