Answer

Word2Vec: static embeddings — one vector per word regardless of context (bank = financial OR river bank). Trained by predicting context (skip-gram) or center word (CBOW). GloVe: global co-occurrence statistics. BERT: contextual embeddings — different vectors for same word in different contexts. Modern standard is contextual embeddings for most NLP tasks.