Zhang Rui, Shan Yifeng, Zhen MengZhe
Business School, Shandong Xiehe University, Jinan, Shandong, China.
School of Basic Education, Ningbo University of Finance and Economics, Ningbo, Zhejiang, China.
Front Med (Lausanne). 2025 Jun 26;12:1578769. doi: 10.3389/fmed.2025.1578769. eCollection 2025.
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.
命名实体识别（NER）在跨专业协作（IPC）和教育中起着关键作用，它提供了一种手段，用于识别和分类对高效跨学科交流和知识共享至关重要的特定领域实体。虽然传统方法，如基于规则的系统和机器学习模型，在各个领域都取得了一定的成功，但它们往往难以应对IPC场景中动态的、上下文敏感的性质。现有方法缺乏对不断演变的术语的适应性，并且没有充分解决多学科框架中固有的复杂交互动态。
为了解决这些限制，我们提出了一个与自适应协同优化策略（ASOS）集成的协同协作框架（SCF）。SCF将IPC建模为一个动态多智能体系统，其中学科被表示为在加权图结构内交互的智能体。每个智能体动态地为协作过程做出贡献，调整其知识、技能和资源，以优化全局效用，同时最小化冲突并增强协同效应。ASOS通过采用实时反馈回路、冲突解决算法和资源重新分配策略来补充这一点，以迭代地改进贡献和交互。
实验评估表明，与基线方法相比，在实体识别准确性、冲突缓解和整体协作效率方面有显著提高。
本研究推进了NER在IPC中的理论和实际应用，确保了对复杂现实世界场景的可扩展性和适应性。
