# What are word embeddings like Word2Vec and GloVe?

Source: https://zilliz.com/ai-faq/what-are-word-embeddings-like-word2vec-and-glove
Author: zilliz

Word embeddings like Word2Vec and GloVe are dense vector representations of words that capture their semantic and syntactic relationships based on co-occurrence patterns in text. These embeddings map words with similar meanings to points that are close together in a high-dimensional space.

Word2Vec uses neural networks to learn embeddings by predicting a word from its context (Skip-Gram) or predicting surrounding words given a word (CBOW). GloVe, on the other hand, uses a matrix factorization approach to capture global co-occurrence statistics from a corpus. Both methods create embeddings that encode the relationships between words, such as analogies (e.g., "king - man + woman = queen").

These embeddings are widely used in NLP tasks like text classification, sentiment analysis, and machine translation. While effective, they are static, meaning a word has the same representation regardless of its context.