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Document-level relation extraction is a complex human process that requireslogical inference to extract relationships between named entities in text.Existing approaches use graph-based neural models with words as nodes and edgesas relations between them, to encode relations across sentences. These modelsare node-based, i.e., they form pair representations based solely on the twotarget node representations. However, entity relations can be better expressedthrough unique edge representations formed as paths between nodes. We thuspropose an edge-oriented graph neural model for document-level relationextraction. The model utilises different types of nodes and edges to create adocument-level graph. An inference mechanism on the graph edges enables tolearn intra- and inter-sentence relations using multi-instance learninginternally. Experiments on two document-level biomedical datasets forchemical-disease and gene-disease associations show the usefulness of theproposed edge-oriented approach.

Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs