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.
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: Text Processing — Tokenization, Stemming, Lemmatization, Naive Bayes (Core ML: Algorithms from Scratch), Naive Bayes
- Time: ~45 minutes
The Problem
Text Processing — Tokenization, Stemming, Lemmatization 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.
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