Entity Relation Joint Extraction with Data Augmentation Based on Large Language Model

Entity relation extraction aims to identify entities and their semantic relationships from unstructured text. To address issues like cascading errors and redundant information found in current joint extraction methods, a One-Module One-Step model is adopted. Additionally, in overcoming challenges related to limited annotated data and the tendency of neural networks to overfit, this paper introduces a method leveraging data augmentation based on a large language model. The approach utilizes five data augmentation strategies to improve the accuracy of triple extraction. Conducting experiments on the augmented dataset reveals significant enhancements in evaluation metrics compared to unaugmented data. In entity relation extraction tasks, the proposed method demonstrates a notable boost, increasing accuracy and F1 scores by 7.3 and 8.5 percentage points, respectively. Moreover, it shows a positive impact on the non-prompting strategy, elevating accuracy and F1 scores by 9.4 and 9.1 percentage points, respectively. These experiments affirm the effectiveness of data augmentation based on a large language model in improving entity relation extraction tasks.