Babel Street Entity and Relationship Mapping
Babel Street’s AI text analytics modules extract and link people, places, organizations, and events across languages — disambiguating similar names, correlating mentions, and connecting entities to knowledge bases for deeper insight.
Extract relevant information from multilingual data
Multilingual Mastery
Perform NLP analysis in 40+ languages and scripts to identify events and entities in unstructured text
Context-Aware Precision
Understand context to disambiguate entities and link them to knowledge base entries for identity resolution
Real-Time, Scalable
Process millions of documents with lightning-fast performance and cloud-scale elasticity
Beyond the Basics
Extract nearly 20 entity types, including people, organizations, and locations
360° Event Intelligence
Detect the date and times of specific events along with the key people, places, and organizations involved
Rapid Model Tuning
Improve accuracy with the ability to train and fine-tune models for domain-specific entities and events
Product Features
Engineered for Entity Intelligence
Extraction capabilities
- Multi-entity extraction — Identify and extract a broad range of entities including people, organizations, locations, dates, times, products, titles, addresses, nationalities, religions, and more.
- Event detection — Extract and categorize events, linking them to associated entities and attributes includingparticipants, times, and locations.
- Sentiment and relationship extraction — Analyze text to detect sentiment, opinion holders, and the relationships among entities.
- Nested entity recognition — Identify complex entities embedded within larger entities.
