NLP & Language · intermediate · concept 118 of 311 · Updated Nov 4, 2026
Word2Vec & Word Embeddings
Representing words as dense vectors where semantic relationships are captured geometrically. The famous result: king - man + woman ≈ queen. Predecessor to modern embeddings used in LLMs. Before neural embeddings, LSA reached similar ground statistically by factorizing the term-document matrix with SVD.
Top AI contributors on this
- Yoshua BengioLawZero / Université de Montréal
- Ilya SutskeverSafe Superintelligence Inc. (SSI)
- Noam ShazeerOpenAI
- Alec RadfordIndependent / Thinking Machines Lab (adviser)
- Jürgen SchmidhuberKAUST / IDSIA
- Oriol VinyalsDiscovery Loop
Key terms
Skip-gramCBOWGloVeCosine similarityEmbedding spaceLSA (latent semantic analysis)
Learn these first
Videos
▶ Word Embedding and Word2Vec, Clearly Explained!!! ↗
StatQuest with Josh Starmer · YouTube
▶ What are Word Embeddings? ↗
IBM Technology · YouTube
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