AI engineering glossary · NLP embeddings
What is word embedding?
Word embedding (also called Word vector, Word representation): A dense list of real numbers, typically 50 to 1,000 long, that represents a word so that words with similar meaning have vectors that are close together. Embeddings are learned from text and let models generalise across related words.
Word embedding: an example
In a good embedding, the vector for "king" is closer to "queen" than to "banana".
Related terms
More NLP embeddings terms
- Word sense disambiguation
- Word tokenisation
- Word2Vec
- WordPiece
- Zipf's law
- Agglutinative language
- Backoff
- Bi-encoder
Where you learn it
Word embedding is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.