Phase 0: NLP Foundations · ~45 minutes · Python

Text Processing — Tokenization, Stemming, Lemmatization

Language is continuous. Models are discrete. Preprocessing is the bridge.

Hiring signal: Understanding of text processing — tokenization, stemming, lemmatization internals

What you will learn

Implement text processing — tokenization, stemming, lemmatization from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 2 · 14 (Naive Bayes) Time: ~45 minutes

The Problem

A model cannot read "The cats were running." It reads integers.

Every NLP system opens with the same three questions. Where does a word start. What is the root of the word. How do we treat "run", "running", "ran" as the same thing when it helps, and as different things when it doesn't.

Get tokenization wrong and the model learns from garbage. If your tokenizer treats don't as one token but do n't as two, the training distribution splits. If your stemmer collapses organization and organ to the same stem, topic modeling dies. If your lemmatizer needs part-of-speech context but you don't pass it, verbs get treated as nouns.

This lesson builds the three preprocessing steps from scratch, then shows how NLTK and spaCy do the same work so you can see the tradeoffs.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Token