Chapter 3

Traditional Approaches to NLP

Before neural networks took over NLP, the field ran on bag-of-words, TF-IDF, N-grams, and Hidden Markov Models. This chapter builds intuition for each — how they work, where they fail, and where they still belong. A TF-IDF logistic regression baseline is still the right first move for most text classification tasks, and understanding why sets up the contrast that motivates everything that comes next.

• 1. Bag of Words→
• 2. TF-IDF and N-grams→
• 3. Hidden Markov Models→
• 4. When to Still Use Traditional ML for NLP→