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.Comment: Accepted by IJCNN 202