In natural language, often multiple entities appear in the same text.
However, most previous works in Relation Extraction (RE) limit the scope to
identifying the relation between two entities at a time. Such an approach
induces a quadratic computation time, and also overlooks the interdependency
between multiple relations, namely the relation of relations (RoR). Due to the
significance of RoR in existing datasets, we propose a new paradigm of RE that
considers as a whole the predictions of all relations in the same context.
Accordingly, we develop a data-driven approach that does not require
hand-crafted rules but learns by itself the RoR, using Graph Neural Networks
and a relation matrix transformer. Experiments show that our model outperforms
the state-of-the-art approaches by +1.12\% on the ACE05 dataset and +2.55\% on
SemEval 2018 Task 7.2, which is a substantial improvement on the two
competitive benchmarks.

本文提出了一种基于图神经网络和关系矩阵变换器的数据驱动关系提取新方法，不需要手工规则，能够完整考虑同一个上下文中的多个关系，并通过对 ACE05 数据集和 SemEval 2018 Task 7.2 等数据集的实验验证，证明该方法优于现有方法 1.12％到 2.55％不等。