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.

本文介绍了一种新颖的边缘编辑方法，旨在从文档中提取关系信息，并将文档中的关系视为实体之间的关系图。我们利用预训练的 transformer 模型和图卷积神经网络模型对边缘进行分类，通过对初始图形的边缘进行编辑，构建关系图。我们将我们的方法应用于从材料科学文本中提取材料合成过程，并进行了实验证明。实验结果表明，我们的方法可以有效地编辑由我们内部基于规则系统和空图形引导的图形。