Chapter 4

Word Embeddings

Bag-of-words is sparse, huge, and semantically meaningless. Word embeddings replace it with dense, low-dimensional vectors where geometric distance reflects semantic similarity. This chapter walks through Word2Vec's deceptively simple architecture, explains why extracting the weight matrix as embeddings works, demonstrates vector arithmetic (king − man + woman ≈ queen), and introduces Doc2Vec and GloVe.

• 1. Why We Need a Better Representation→
• 2. Word2Vec→
• 3. The Most Famous Equation in NLP→
• 4. The Limitation of Word Embeddings→