Phase 1: Text Representation · ~45 minutes · Python

POS Tagging and Syntactic Parsing

Grammar was unfashionable for a while. Then every LLM pipeline needed to validate structured extraction, and it came back.

Hiring signal: Understanding of pos tagging and syntactic parsing internals

What you will learn

Implement pos tagging and syntactic parsing 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 5 · 01 (Text Processing), Phase 2 · 14 (Naive Bayes) Time: ~45 minutes

The Problem

Lesson 01 promised that lemmatization needs a part-of-speech tag. Without knowing running is a verb, a lemmatizer cannot reduce it to run. Without knowing better is an adjective, it cannot reduce to good.

That promise hid a whole subfield. Part-of-speech tagging assigns grammatical categories. Syntactic parsing recovers the sentence's tree structure: which word modifies which, which verb governs which arguments. Classical NLP spent twenty years refining both. Then deep learning collapsed them into a token-classification task on top of a pretrained transformer, and the research community moved on.

Not the applied community. Every structured-extraction pipeline still uses POS and dependency trees under the hood. LLM-generated JSON gets validated against grammatical constraints. Question-answering systems decompose queries using dependency parses. Machine translation quality evaluators check alignment of parse trees.

Worth knowing. This lesson introduces the tagsets, the baselines, and the point where you stop implementing from scratch and call spaCy.

Unlock the full lesson

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