What is Named Entity Recognition (NER)? - Definition & Meaning
Learn what NER is, how AI extracts names, organizations, and dates from text, and why NER is essential for document processing and knowledge graphs.
Definition
Named Entity Recognition (NER) is an NLP task that identifies and classifies named real-world entities in text — persons, organizations, locations, dates, amounts, and other named concepts.
Technical explanation
NER produces tagged spans: [John Smith]PER, [Amsterdam]LOC, [2026]DATE. Methods: rule-based (regex, gazetteers), statistical (CRF, HMM), and deep learning (LSTM, BERT-based). Modern NER uses transformer models and fine-tuning on labeled data. Entity linking connects entities to knowledge bases (Wikidata, DBpedia). NER is a building block for information extraction, knowledge graph construction, and document processing.
How AVARC Solutions applies this
AVARC Solutions applies NER in contract analysis, CV screening, invoice processing, and knowledge graph population. We use state-of-the-art models and train on domain-specific entities (product names, customer codes) when needed.
Practical examples
- A contract tool automatically extracting parties, dates, amounts, and clauses for a structured overview.
- A CV parser identifying names, education, employers, and skills for recruiters.
- A news monitor extracting companies, people, and events from articles for reputation monitoring.
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