NLP BasicsLesson 3
Lesson 38 min
Word Embeddings
Turning words into vectors that capture meaning.
What you will learn
- ✓What embeddings represent
- ✓How similarity is measured
- ✓The famous king-queen analogy
Explanation
An embedding maps each word (or token) to a vector of numbers so that similar meanings sit close together in that space.
This lets you measure similarity mathematically — 'happy' is near 'glad', far from 'tractor'. Early methods like word2vec even captured analogies: king − man + woman ≈ queen.
Embeddings are the foundation of semantic search, recommendation, and RAG.
Real-world use
Product search that understands 'budget phone' matches 'cheap smartphone' thanks to embeddings.
Common mistakes
- • Treating embeddings as exact word lists rather than positions in a meaning-space.
Practice
Name two word pairs that should be close in embedding space and two that should be far apart.
Knowledge check
0/1 answered1. Word embeddings place similar words...
Answer all questions to check.