SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
The paper presents a novel method called Structured Semantic Data Augmentation (SSDAU) aimed at improving Joint Entity and Relation Extraction (JERE). SSDAU focuses on preserving the semantic structure of text during data augmentation, addressing issues with existing methods that disrupt text relevance. Experimental results show that SSDAU significantly outperforms traditional data augmentation techniques in generating semantically consistent data.
- ▪SSDAU is designed to enhance model generalization for Joint Entity and Relation Extraction.
- ▪The method preserves semantic structures during text augmentation by segmenting based on entity labels.
- ▪Experiments indicate that SSDAU achieves superior robustness against ambiguity compared to existing methods.
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Computer Science > Computation and Language arXiv:2605.23440 (cs) [Submitted on 22 May 2026] Title:SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Authors:Jiawei He, Mengyu Shi, Chunrong Fang View a PDF of the paper titled SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction, by Jiawei He and 2 other authors View PDF HTML (experimental) Abstract:Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains.
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