AGNER: Agile governance-oriented unified named entity recognition for continual learning with diffusion adaptation.

Area of Science:

• Natural Language Processing
• Machine Learning
• Information Extraction

Background:

• Agile governance demands efficient information extraction from growing text data streams.
• Traditional Named Entity Recognition (NER) models struggle with adaptability and catastrophic forgetting in dynamic environments.
• Existing NER methods lack robustness for evolving entity knowledge in governance contexts.

Purpose of the Study:

• To develop a unified framework, AGNER (Agile Governance-Oriented Unified Named Entity Recognition), for robust NER in agile governance.
• To enhance the adaptability and resilience of NER models to evolving data and entity types.
• To address challenges of catastrophic forgetting and domain specificity in continual learning settings.

Main Methods:

• Creation of a fine-grained NER dataset for governance texts, including complex entity structures (flat, nested, discontinuous).
• Implementation of a diffusion-based learning process with entity grid boundary modeling for supervised NER.
• Integration of a diffusion memory buffer, gradient alignment, and distillation for continual learning scenarios.

Main Results:

• AGNER achieved a 0.47% average F1 score improvement over state-of-the-art models in supervised learning.
• AGNER demonstrated a 6% improvement in forgetting resistance in continual learning settings.
• The framework showed enhanced adaptation capabilities for newly emerging entities.

Conclusions:

• AGNER provides a robust and adaptable solution for Named Entity Recognition in agile governance.
• The proposed framework effectively mitigates catastrophic forgetting and improves model performance in dynamic environments.
• AGNER facilitates more accurate and rapid extraction of actionable information from large-scale governance text data.