midNLP
What is word embedding?
Updated May 17, 2026
Short answer
Word embeddings represent words as dense vectors capturing semantic meaning.
Deep explanation
Embeddings map words into continuous vector space where semantic similarity is preserved using models like Word2Vec, GloVe, or FastText.
Real-world example
Recommendation systems use embeddings for similarity matching.
Common mistakes
- Thinking embeddings are interpretable dimensions.
Follow-up questions
- What is cosine similarity?
- How does Word2Vec learn embeddings?