A Joint Extraction Model of Multiple Chinese Emergency Event–Event Relations Based on Weighted Double Consistency Constraint Learning

Event–event relation extraction (ERE) is an important and challenging task in natural language processing. At present, state-of-the-art ERE methods mainly adopt supervised learning, especially deep learning, which needs a large number of high-quality labeled event corpora. However, these methods will face the challenge of few-shot learning for extracting Chinese multiple event–event relations. Complex deep learning models often cannot converge effectively on small Chinese event corpora. And the manual event relation labeling is a very time-consuming and uncertain work. This paper proposes a joint extraction model for multiple Chinese event–event relations based on weighted double consistency constraint learning, named the Chinese event–event relations miner (CERMiner), to extract multiple types of Chinese emergency event–event relations jointly. After encoding event pairs from their contexts, a group of weighted double consistency constraint, including common sense constraints and domain constraints, are designed and integrated into model learning to accelerate model convergence on few-shot corpora. To evaluate the effectiveness of the CERMiner model, we conduct experiments on the CEC dataset, which contains three relation types—CE, EC, and AC—with 697, 200, and 242 instances, respectively. We report Precision, Recall, and F1-score as evaluation metrics. Our method achieves 84.8%, 72.7%, and 78.2% in Precision, Recall, and F1-score, respectively, outperforming the SGT baseline by 1.7% in F1-score. These results demonstrate that the proposed model can better realize joint extraction of multiple Chinese emergency event–event relations in low-resource environments compared to existing state-of-the-art methods.