Named Entity Recognition: Extracting Information from Text

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.