TF-IDF and Bag of Words: Text Representation & Search › TF-IDF and Bag of Words: Classical text representation methods and their applications
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Before neural networks and transformers, how did we convert text into numbers that machines could process? The answer lies in two foundational techniques that remain relevant today: Bag of Words and TF-IDF (Term Frequency-Inverse Document Frequency).
These methods solve a fundamental problem in NLP: text is inherently discrete and symbolic, but most machine learning algorithms require numerical input. Bag of Words provides a simple way to count word occurrences, while TF-IDF adds sophistication by weighting words based on their importance across a document collection.
In this chapter, we'll explore how these classical techniques work, implement them from scratch, and understand both their power and limitations. You'll see how they enable everything from search engines to document classification, and why they're still used in production systems today.