Named Entity Recognition
NER
NLP technique that identifies and classifies named entities in text - people, organisations, locations, dates, monetary values, and other specific information.
In-Depth Explanation
Named Entity Recognition (NER) extracts and classifies specific information from unstructured text. It's essential for turning documents into structured, actionable data.
Common entity types:
- PERSON: People's names
- ORG: Organisations, companies
- LOC/GPE: Locations, geopolitical entities
- DATE/TIME: Temporal expressions
- MONEY: Monetary values
- PRODUCT: Product names
- Custom entities: Industry-specific types
NER approaches:
- Rule-based: Patterns, dictionaries, regex
- Statistical: CRF, HMM models
- Deep learning: BiLSTM-CRF, transformers
- LLM-based: Prompting for entity extraction
Business applications:
- Contract analysis (parties, dates, amounts)
- Resume parsing (skills, experience, education)
- News analysis (companies, people, events)
- Customer data extraction
- Compliance monitoring
Business Context
NER transforms unstructured documents into structured data - extracting parties from contracts, details from emails, and information from forms without manual data entry.
How Clever Ops Uses This
We implement NER solutions for Australian businesses to automate data extraction from contracts, invoices, customer communications, and compliance documents.
Example Use Case
"Automatically extracting company names, contact details, and key terms from thousands of supplier contracts for migration to a contract management system."