Phase 0: NLP Foundations · ~75 minutes · Python

Named Entity Recognition

Pull the names out. Sounds easy until you deal with ambiguous boundaries, nested entities, and domain jargon.

Hiring signal: Understanding of named entity recognition internals

What you will learn

Implement named entity recognition 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 · 02 (BoW + TF-IDF), Phase 5 · 03 (Word Embeddings) Time: ~75 minutes

The Problem

"Apple sued Google over its iPhone search deal in the US." Five entities: Apple (ORG), Google (ORG), iPhone (PRODUCT), search deal (maybe), US (GPE). A good NER system extracts all of them with correct types. A bad one misses iPhone, confuses Apple the fruit with Apple the company, and labels "US" as a PERSON.

NER is the workhorse underneath every structured extraction pipeline. Resume parsing, compliance log scanning, medical record anonymization, search query understanding, grounding for chatbot responses, legal contract extraction. You never quite see it; you always depend on it.

This lesson walks the classical path (rule-based, HMM, CRF) into the modern one (BiLSTM-CRF, then transformers). Each step solves a specific limitation of the one before it. The pattern is the lesson.

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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