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

Source: [SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction](<https://www.sota2.com/research/paper/6a13d7646f71c5ae7dc9e763>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-05-30T05:00:11.986Z

Authors: Jiawei He, Mengyu Shi, Jiawei Liu, Dong Sun, Chunrong Fang, Xikai Yang, Zhijie Wang, Lei Ma, Zhenyu Chen

Published: 2026-05-22T09:52:43.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Joint Entity and Relation Extraction on WebNLG](<https://www.sota2.com/research/sota/joint-entity-and-relation-extraction-on-webnlg.md>): Joint Entity and Relation Extraction; WebNLG; Precision: 92.8.

- [Joint Entity and Relation Extraction on NYT](<https://www.sota2.com/research/sota/joint-entity-and-relation-extraction-on-nyt.md>): Joint Entity and Relation Extraction; NYT; Precision: 92.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2605.23440>)

[PDF](<https://arxiv.org/pdf/2605.23440.pdf>)