Document-level relation extraction is a complex human process that requires
logical inference to extract relationships between named entities in text.
Existing approaches use graph-based neural models with words as nodes and edges
as relations between them, to encode relations across sentences. These models
are node-based, i.e., they form pair representations based solely on the two
target node representations. However, entity relations can be better expressed
through unique edge representations formed as paths between nodes. We thus
propose an edge-oriented graph neural model for document-level relation
extraction. The model utilises different types of nodes and edges to create a
document-level graph. An inference mechanism on the graph edges enables to
learn intra- and inter-sentence relations using multi-instance learning
internally. Experiments on two document-level biomedical datasets for
chemical-disease and gene-disease associations show the usefulness of the
proposed edge-oriented approach.

本研究采用面向边的图神经网络模型对文档级关系抽取进行研究，该模型利用不同类型的节点和边来创建文档级图，并通过图上边的推理机制进行联合及交叉句子的学习，实验结果表明该方法在化学疾病和基因疾病相关性数据集上表现出很好的性能。