论文标题
关系的关系：关系提取问题的新范式
Relation of the Relations: A New Paradigm of the Relation Extraction Problem
论文作者
论文摘要
在自然语言中，通常多个实体出现在同一文本中。但是，大多数以前的关系提取（RE）的工作将范围限制为一次识别两个实体之间的关系。这种方法诱导了二次计算时间，也忽略了多个关系之间的相互依赖性，即关系的关系（ROR）。由于ROR在现有数据集中的重要性，我们提出了一个新的RE范式，该范式整体认为在同一上下文中所有关系的预测。因此，我们开发了一种数据驱动的方法，该方法不需要手工制作的规则，而是使用图形神经网络和关系矩阵变压器自行学习ROR。实验表明，在ACE05数据集上，我们的模型优于最先进的方法，而在Semeval 2018 Task 7.2上，我们的模型在ACE05数据集中的方法+1.12 \％，这是对两个竞争性基准的实质性改进。
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.
