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.