Word Embedding

A word embedding is a dense, low-dimensional vector representation of a word learned from large text corpora. Unlike sparse one-hot or bag-of-words vectors, embeddings encode semantic and syntactic relatedness as geometric proximity. Pioneered by methods like Word2Vec and GloVe, word embeddings became the dominant text representation in pre-transformer NLP, serving as input to recurrent networks and convolutional text classifiers. Contextual embeddings from transformers later superseded static word embeddings for most tasks.