Content Classification engines in the Veritone cognitive engine ecosystem categorize one or more documents or text files into predefined categories based on what words the text contains. Classification examples might include Finance, Internet, News, or Real Estate, with sub-categories available for further sub-classification. Categorizing text in this way can accelerate request routing and improve records management.
Content Classification Features:
- Multiple Language Support
Classify text in multiple different natural languages including English, Russian, Arabic, Spanish, French, and Chinese to support a diverse user-base, workforce, or population.
- Broad Pre-Trained Taxonomy Libraries Support
- Near Real-Time Processing
Process text files in near real-time for use cases requiring content classification for fast analysis at scale.
- Long-Form Text Support
Classify short-form or long-form text in files.
- Flexible Deployment
Deploy in a new or integrate into an existing application in the cloud via aiWARE GraphQL APIs, or with a subset that can be deployed on-premise via a Docker container. Learn more.
- Powered by an AI Ecosystem
Leverage advanced content classification machine learning algorithms from the Veritone managed cognitive engine ecosystem — including algorithms from Veritone, niche providers, and industry giants.