Overlapping entity relation extraction has received extensive research attention in recent years. However, existing methods suffer from the limitation of long-distance dependencies between entities, and fail to extract the relations when the overlapping situation is relatively complex. This issue limits the performance of the task. In this paper, we propose an end-to-end neural model for overlapping relation extraction by treating the task as a quintuple prediction problem. The proposed method first constructs the entity graphs by enumerating possible candidate spans, then models the relational graphs between entities via a graph attention model. Experimental results on five benchmark datasets show that the proposed model achieves the current best performance, outperforming previous methods and baseline systems by a large margin. Further analysis shows that our model can effectively capture the long-distance dependencies between entities in a long sentence.

重叠实体关系提取近年来受到了广泛的研究关注。然而，现有方法受到实体之间的远距离依赖性的限制，并且当重叠情况相对复杂时不能提取关系。此问题限制了任务的性能。在本文中，我们通过将任务视为五元组预测问题，提出了一种用于重叠关系提取的端到端神经模型。该方法首先通过枚举可能的候选跨度来构造实体图，然后通过图注意力模型对实体之间的关系图进行建模。在五个基准数据集上的实验结果表明，所提出的模型达到了目前的最佳性能，大大优于以前的方法和基线系统。