Blackboard Model for Rich Text Annotation

Abstract

This project investigates the application of a Blackboard model for rich-text annotation, focusing on the research, development, and practical use of such a model. The research method employed in this project is the construction of a blackboard model artifact specifically designed for rich-text annotation. This model allows various annotation components to operate independently and in parallel, based on their input-output dependencies rather than a hier- archical structure. This significantly enhances the system’s flexibility. The primary aim is to create a model that supports the seamless replacement of components, ensuring future-proofing and adaptability as NLP technologies evolve. The main findings of this research demonstrate that a Blackboard model can effectively manage multiple, independent annotation components, fa- cilitating easier updates and integration of new technologies. This flexibil- ity addresses a critical gap in existing systems, which often struggle with scalability and adaptability. The implications of these findings suggest that adopting a Blackboard model for text annotation can lead to more resilient and maintainable NLP systems, providing a practical solution for researchers and developers working with large datasets of raw text.