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.

本研究通过整合时间和因果关系的方式，提出 TC-GAT 模型并用于因果关系提取任务，在编译了包括时间关系在内的数据集基础上，进行了实验表明其比基线模型更为有效。

TC-GAT：用于时序因果发现的图注意力网络

TC-GAT: Graph Attention Network for Temporal Causality Discovery

Causality extraction from natural language texts is a challenging open
problem in artificial intelligence. Existing methods utilize patterns,
constraints, and machine learning techniques to extract causality, heavily
depending on domain knowledge and requiring considerable human effort and time
for feature engineering. In this paper, we formulate causality extraction as a
sequence labeling problem based on a novel causality tagging scheme. On this
basis, we propose a neural causality extractor with the BiLSTM-CRF model as the
backbone, named SCITE (Self-attentive BiLSTM-CRF wIth Transferred Embeddings),
which can directly extract cause and effect without extracting candidate causal
pairs and identifying their relations separately. To address the problem of
data insufficiency, we transfer contextual string embeddings, also known as
Flair embeddings, which are trained on a large corpus in our task. In addition,
to improve the performance of causality extraction, we introduce a multihead
self-attention mechanism into SCITE to learn the dependencies between causal
words. We evaluate our method on a public dataset, and experimental results
demonstrate that our method achieves significant and consistent improvement
compared to baselines.

论文提出了一种基于序列标注的原因提取方法，使用 SCITE，一个基于 BiLSTM-CRF 的神经因果提取器，并引入多头自注意力机制和 Flair embeddings 以提高性能，并在公共数据集上进行实验，结果表明相比于基线方法，该方法取得了显著且一致的提升。