SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

Researchers propose SSDAU, a novel data augmentation method for Joint Entity and Relation Extraction that preserves semantic structure and context awareness. The approach significantly outperforms existing methods by reducing F1 score degradation to 8.26% compared to 31.91% for baseline approaches, addressing a critical challenge in NLP model generalization.

SSDAU addresses a fundamental challenge in natural language processing: training models that generalize well across different domains when quality training data is limited. The research tackles Joint Entity and Relation Extraction, a complex NLP task requiring models to simultaneously identify entities and relationships within text. Traditional data augmentation methods struggle with this task because they often break semantic dependencies or alter meaning, resulting in poor-quality synthetic training examples that actually harm model performance.

The innovation lies in SSDAU's structured approach to preserving semantic integrity during augmentation. By segmenting text based on entity labels and using contextualized embeddings combined with traditional similarity metrics, the method maintains the semantic relationships that models need to learn. The integration of BERTTopic filtering further ensures augmented data remains topically consistent and relevant. This represents an evolution beyond naive augmentation techniques that simply swap words or phrases without understanding their contextual importance.

The performance gains are substantial and broadly applicable. SSDAU demonstrates consistent improvements across multiple JERE models and different annotation schemas, suggesting the technique generalizes well. The significantly lower F1 score degradation under adversarial conditions indicates the method produces more robust training data that helps models maintain performance when encountering unfamiliar patterns.

For the machine learning community, this research offers practical value for teams working with limited labeled data across various domains. The structured semantic preservation approach may inspire similar techniques for other NLP tasks. As organizations increasingly deploy language models in production, methods that improve generalization through better data augmentation become economically valuable, potentially reducing annotation costs while maintaining or improving model performance.

• →SSDAU preserves semantic structure during data augmentation by using entity-aware segmentation and contextualized embeddings.
• →Method reduces F1 score degradation to 8.26% compared to 31.91% for baseline augmentation approaches.
• →Incorporation of BERTTopic filtering ensures topic consistency and prevents information loss in augmented data.
• →Approach demonstrates consistent improvements across five different JERE models and multiple annotation types.
• →Technique addresses practical challenge of improving NLP model generalization with limited, low-quality training data.