Stanford SPIED
Stanford SPIED is a software tool that utilizes bootstrapped pattern-based learning to extract entities from unlabeled text. By inputting seed sets of predefined entity classes, it identifies and outputs related entities, patterns, and JSON visualizations, aiding researchers in enhancing information extraction processes efficiently. The system supports multi-class learning and offers customizable settings for diverse applications.
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Top Stanford SPIED Features
- Bootstrapped learning algorithm
- Multi-class entity extraction
- Customizable input seed sets
- Visualization output in JSON
- Iterative pattern learning
- User-friendly properties file
- Comprehensive documentation provided
- Support for unlabeled text
- Efficient extraction from large datasets
- Enhanced accuracy in entity recognition
- Integration with Stanford CoreNLP
- Example data for quick setup
- Support for multiple entity types
- Real-time pattern visualization
- Open-source licensing for research
- Beta version with active testing
- Detailed FAQ for user support
- Email support from developers
- Regular updates and release history.
