In this paper, we propose a novel edge-editing approach to extract relation information from a document. We treat the relations in a document as a relation graph among entities in this approach. The relation graph is iteratively constructed by editing edges of an initial graph, which might be a graph extracted by another system or an empty graph. The way to edit edges is to classify them in a close-first manner using the document and temporally-constructed graph information; each edge is represented with a document context information by a pretrained transformer model and a graph context information by a graph convolutional neural network model. We evaluate our approach on the task to extract material synthesis procedures from materials science texts. The experimental results show the effectiveness of our approach in editing the graphs initialized by our in-house rule-based system and empty graphs.

翻译：在本文中,我们提出一种新的边际编辑方法,从文档中提取关系信息。我们在一份文件中将关系作为实体间的关系图表处理。关系图由初始图形的编辑边缘迭代构建,初步图形的编辑边缘可能是由另一个系统或空图提取的图表。编辑边缘的方法是使用文档和时间构建的图表信息,以近距离第一的方式对其进行分类;每个边缘以预先训练的变压器模型作为文件背景信息表示,图形神经网络模型作为图表背景信息表示。我们评估了我们从材料科学文本中提取材料合成程序的任务的方法。实验结果显示了我们在编辑我们内部基于规则的系统和空图时所采用的图表方法的有效性。