Advancing named entity recognition in interprofessional collaboration and education

TL;DR

This paper introduces a new framework to improve named entity recognition in interprofessional collaboration and education by modeling it as a dynamic multi-agent system.

Contribution

The novel Synergistic Collaboration Framework with Adaptive Synergy Optimization Strategy enhances adaptability and accuracy in NER for dynamic IPC scenarios.

Findings

The proposed framework significantly improves entity recognition accuracy compared to baseline methods.

Conflict mitigation and collaboration efficiency are enhanced through real-time feedback and resource reallocation.

The approach demonstrates scalability and adaptability to evolving terminologies in multi-disciplinary settings.

Abstract

Named Entity Recognition (NER) plays a critical role in interprofessional collaboration (IPC) and education, providing a means to identify and classify domain-specific entities essential for efficient interdisciplinary communication and knowledge sharing. While traditional methods, such as rule-based systems and machine learning models, have achieved moderate success in various domains, they often struggle with the dynamic, context-sensitive nature of IPC scenarios. Existing approaches lack adaptability to evolving terminologies and insufficiently address the complex interaction dynamics inherent in multi-disciplinary frameworks.

To address these limitations, we propose a Synergistic Collaboration Framework (SCF) integrated with an Adaptive Synergy Optimization Strategy (ASOS). SCF models IPC as a dynamic multi-agent system, where disciplines are represented as intelligent agents interacting within a weighted graph structure. Each agent contributes dynamically to the collaborative process, adapting its knowledge, skills, and resources to optimize global utility while minimizing conflicts and enhancing synergy. ASOS complements this by employing real-time feedback loops, conflict resolution algorithms, and resource reallocation strategies to iteratively refine contributions and interactions.

Experimental evaluations demonstrate significant improvements in entity recognition accuracy, conflict mitigation, and overall collaboration efficiency compared to baseline methods.

This study advances the theoretical and practical applications of NER in IPC, ensuring scalability and adaptability to complex, real-world scenarios.

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