The present study explores the intricacies of causal relationship extraction, a vital component in the pursuit of causality knowledge. Causality is frequently intertwined with temporal elements, as the progression from cause to effect is not instantaneous but rather ensconced in a temporal dimension. Thus, the extraction of temporal causality holds paramount significance in the field. In light of this, we propose a method for extracting causality from the text that integrates both temporal and causal relations, with a particular focus on the time aspect. To this end, we first compile a dataset that encompasses temporal relationships. Subsequently, we present a novel model, TC-GAT, which employs a graph attention mechanism to assign weights to the temporal relationships and leverages a causal knowledge graph to determine the adjacency matrix. Additionally, we implement an equilibrium mechanism to regulate the interplay between temporal and causal relations. Our experiments demonstrate that our proposed method significantly surpasses baseline models in the task of causality extraction.

翻译：本研究探讨了因果关系抽取这一因果关系知识构建中的关键复杂问题。因果关系常与时间要素紧密交织，因为从原因到结果的演变并非瞬时完成，而是嵌入时间维度之中。因此，时序因果关系的抽取在该领域具有至关重要的意义。基于此，我们提出了一种从文本中抽取因果关系的方法，该方法整合了时间与因果双重关系，并特别关注时间维度。为此，我们首先构建了一个包含时间关系的数据集。随后，我们提出了一种新颖模型TC-GAT，该模型采用图注意力机制为时间关系分配权重，并借助因果知识图谱确定邻接矩阵。此外，我们还引入平衡机制以调控时间关系与因果关系之间的相互作用。实验结果表明，所提出的方法在因果关系抽取任务上显著优于基线模型。