Nested relation extraction via self-contrastive learning guided by structure and semantic similarity.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Conventional relation extraction (RE) struggles with complex, nested relationships in natural language.
• Existing nested RE methods face challenges like insufficient labeled data and suboptimal neural network architectures.
• Nested relation structures are often underutilized, limiting extraction capabilities.

Purpose of the Study:

• To formalize the nested RE task and address its inherent challenges.
• To propose a novel hierarchical neural network for iterative nested relation identification.
• To develop a self-contrastive learning strategy for low-data nested RE scenarios.

Main Methods:

• Formalization of the nested RE task.
• Development of a hierarchical neural network for layer-by-layer extraction of nested relations.
• Introduction of a self-contrastive learning optimization strategy leveraging nested structure and semantic similarity.

Main Results:

• The proposed hierarchical neural network significantly outperformed state-of-the-art baseline methods.
• Ablation experiments confirmed the effectiveness of the self-contrastive learning strategy.
• The method demonstrates improved performance in extracting complex, nested relations.

Conclusions:

• The proposed hierarchical neural network effectively addresses the challenges of nested relation extraction.
• The self-contrastive learning strategy offers a robust solution for low-data settings in nested RE.
• This work advances the field of information extraction by enabling more sophisticated relation identification.