Phase 4: NLP Applications · ~60 minutes · Python
Entity Linking & Disambiguation
NER found "Paris." Entity linking decides: Paris, France? Paris Hilton? Paris, Texas? Paris (the Trojan prince)? Without linking, your knowledge graph stays ambiguous.
Hiring signal: Understanding of entity linking & disambiguation internals
What you will learn
- Implement entity linking & disambiguation 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 · 06 (NER), Phase 5 · 24 (Coreference Resolution) Time: ~60 minutes
The Problem
A sentence reads: "Jordan beat the press." Your NER tags "Jordan" as PERSON. Good. But which Jordan?
- Michael Jordan (basketball)?
- Michael B. Jordan (actor)?
- Michael I. Jordan (Berkeley ML professor — yes, this confusion is real in ML papers)?
- Jordan (the country)?
- Jordan (Hebrew first name)?
Entity linking (EL) resolves each mention to a unique entry in a knowledge base: Wikidata, Wikipedia, DBpedia, or your domain KB. Two subtasks:
- Candidate generation. Given "Jordan," which KB entries are plausible?
- Disambiguation. Given the context, which candidate is the right one?
Both steps are learnable. Both are benchmarked. The combined pipeline has been stable for a decade — what changes is the quality of the disambiguator.
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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Pitfalls, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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