Word embedding is a technique used in NLP to represent words as dense vectors in a continuous vector space, capturing semantic relationships between them. Unlike traditional methods like Bag of Words, which represent words as sparse vectors, embeddings encode words with rich contextual information. For example, "king" and "queen" might have vectors close to each other in the embedding space, reflecting their related meanings.
Popular word embedding methods include Word2Vec, GloVe, and fastText. Word2Vec uses neural networks to learn word relationships based on co-occurrence in a corpus, producing embeddings where relationships like "king - man + woman = queen" can be observed. GloVe combines global and local statistical information to create embeddings that capture broader patterns in text.
Modern NLP models, such as BERT and GPT, take embeddings further by generating context-sensitive representations. This means that the embedding of a word like "bank" will differ based on whether it appears in the context of finance or a river. Word embeddings are foundational for deep learning in NLP, enabling tasks like text classification, sentiment analysis, and machine translation to achieve high performance.