In this episode, we explore the embedding revolution in natural language processing—the moment NLP moved from counting words to learning meaning. We trace how dense vector representations transformed language into a geometric space, enabling models to capture similarity, analogy, and semantic structure for the first time. This shift laid the groundwork for everything from modern search to large language models.

This episode covers:

• Why bag-of-words and TF-IDF failed to capture meaning

• The distributional hypothesis: “you know a word by the company it keeps”

• Dense vs. sparse representations and why geometry matters

• Topic models as early semantic compression (LSI, LDA)

• Word2Vec: CBOW and Skip-Gram

• Vector arithmetic and semantic analogies

• GloVe and global co-occurrence statistics