algo-nlp-ner
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
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