A word embedding is a type of embedding specifically used in Natural Language Processing (NLP). It maps words (or subwords) to real-valued vectors in a continuous vector space, where semantically similar words are close together.

Example word embeddings:

• Word2Vec
• GloVe
• FastText
• BERT (contextual embeddings)

Properties:

• Vectors are typically 50 to 1,024 dimensions
• Similar meanings → similar vectors (cosine similarity)

Example:

word_vectors["king"] - word_vectors["man"] + word_vectors["woman"] ≈ word_vectors["queen"]

See: Cosine Similarity