提出结构化语义数据增强方法，提升联合实体关系抽取的泛化能力。
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
- 按实体标签分段，用上下文编码保持语义结构
- 生成数据在五种模型上平均F1提升8.95%，优于主流基线
- 适合需要高质量数据增强的实体关系抽取任务
联合实体与关系抽取（JERE）对训练数据质量高度敏感，数据增强成为提升泛化能力的自然选择。然而，现有增强方法常削弱实体相关性并破坏语义结构，限制其在JERE中的效果。本文提出结构化语义数据增强（SSDAU），旨在增强过程中保留三元组感知的语义结构。SSDAU按实体标签分割文本，通过上下文感知编码捕捉语义特征，并重构实体语义以生成新数据。为区分语义相似实体，结合上下文嵌入与传统相似度评分；为减少主题不一致，采用BERTopic进行过滤，剔除无关增强。我们在不同标注类型的多个数据集上评估，对比五种代表性JERE模型与七种主流增强基线。实验表明，SSDAU生成的数据具有更高语义一致性，对模糊情况更鲁棒（非大模型方法平均F1下降23.58% vs. SSDAU仅8.95%），且在多数设置下显著优于强基线。
原文摘要 · Abstract (English)
Joint Entity and Relation Extraction (JERE) is highly sensitive to training data quality, making data augmentation a natural way to improve generalization. However, existing augmentation methods often weaken entity relevance and disrupt semantic structure, limiting their effectiveness for JERE. In this paper, we propose \textbf{Structured Semantic Data Augmentation (SSDAU)}, a method designed to preserve triple-aware semantic structure during augmentation. SSDAU segments text by entity labels, captures semantic features through context-aware encoding, and restructures entity semantics to generate augmented data. To distinguish semantically similar entities, SSDAU combines contextualized embeddings with traditional similarity scores. To reduce topic inconsistency, we apply BERTopic-based filtering to remove irrelevant augmentations. We evaluate SSDAU on datasets with different annotation types and compare its performance on five representative JERE models against seven popular augmentation baselines. Experiments show that SSDAU generates semantically consistent data, is more robust to ambiguity than non-LLM methods (8.95\% vs. 23.58\% average relative F1 decrease), and significantly outperforms strong alternatives in most settings.
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