Named Entity Recognition

Overview

NER identifies and classifies named entities in text into predefined categories (Person, Organization, Location, Date, Money, etc.). Approaches: rule-based (regex, gazetteers), statistical (CRF), neural (BiLSTM-CRF, transformer-based). Modern NER uses spaCy or Hugging Face models with F1 scores 85-95%.

When to Use

Trigger conditions:

• Extracting structured entities from unstructured text
• Building knowledge graphs from documents
• Preprocessing for information retrieval or question answering

When NOT to use:

• For text classification (categorizing whole documents, not extracting entities)
• For relation extraction between entities (need additional RE model)

Algorithm