  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.
