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NLP
Named Entity Recognition: Extracting Information from Text
Learn how NER identifies and classifies entities in text. Build systems that extract names, dates, and key information.
Ankit 8 min readJanuary 1, 2025
What is Named Entity Recognition?
NER is an NLP task that identifies and classifies named entities in text into predefined categories such as person names, organizations, locations, dates, and more.
Entity Types
Standard Entities
- PERSON: People's names
- ORG: Organizations
- GPE: Geopolitical entities
- DATE: Dates and times
- MONEY: Monetary values
Domain-Specific Entities
- Medical: Diseases, medications
- Legal: Case numbers, laws
- Financial: Ticker symbols, accounts
Applications
Document Processing
Extract key information from contracts, invoices, reports.
Search Enhancement
Improve search by understanding entities.
Knowledge Graphs
Build structured knowledge from text.
Compliance
Identify PII for data protection.
NER Approaches
Rule-Based
- Regular expressions
- Dictionaries
- Gazetteers
Statistical
- CRF (Conditional Random Fields)
- Hidden Markov Models
Deep Learning
- BiLSTM-CRF
- Transformers
- BERT-based models
Building NER Systems
- Define entity types for your domain
- Create annotated training data
- Choose appropriate model architecture
- Train and evaluate
- Handle edge cases
- Deploy and monitor
Challenges
- Ambiguous entities
- Nested entities
- Domain adaptation
- Multi-language support
Conclusion
NER is essential for extracting structured information from unstructured text, enabling automated document processing and analysis.
NERInformation ExtractionText Analysis
Next step
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