← Natural Language Processing learning pathNatural Language Processing · Module 5 of 10
Named entities and information extraction
Pull names, places and facts out of text.
📖 Key ideas
- NER finds entities: person, organisation, location, date, money.
- Part-of-speech tagging labels nouns, verbs, etc.
- Dependency parsing finds grammatical relationships.
- Relation extraction links entities (Sundar Pichai → CEO of Google).
- spaCy provides ready NER models.
💡 Real-life example: NER is a highlighter marking names in blue, places in green and dates in yellow.
NERNamed Entity Recognition
POS taggingPart-of-speech labelling
Dependency parsingGrammar relationships
Relation extractionFinding links between entities