arXiv:2605.23440v1 Announce Type: cross 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. However, existing data augmentation methods often overlook text relevance and may disrupt semantic structures and dependencies, making it difficult to generate effective augmented data for improving model generalization. In this paper, we propose Structured Semantic Data Augmentation (SSDAU), a novel method designed to preserve
The continuous drive for more robust and generalizable AI models in NLP, especially for complex tasks like Joint Entity and Relation Extraction, makes innovations in data augmentation crucial.
Improved data augmentation techniques like SSDAU can significantly enhance the performance and reliability of AI systems, leading to more accurate information extraction and reduced dependence on massive labeled datasets.
This research introduces a method that addresses key limitations in current data augmentation by preserving semantic structures, potentially leading to more effective and less 'noisy' augmented datasets for AI training.
- · AI/ML researchers
- · NLP developers
- · Industries relying on information extraction
- · Existing generic data augmentation methods
AI models, particularly in NLP, will show improved generalization capabilities and reduced training data requirements.
This could accelerate the deployment of AI systems in nuanced, data-scarce domains where current model performance is limited by data quality.
More robust AI for information extraction could underpin advancements in knowledge graph construction and automated textual analysis, further collapsing white-collar workflows.
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Read at arXiv cs.AI