  Relation extraction aims to classify the relationships between two entities
into pre-defined categories. While previous research has mainly focused on
sentence-level relation extraction, recent studies have expanded the scope to
document-level relation extraction. Traditional relation extraction methods
heavily rely on human-annotated training data, which is time-consuming and
labor-intensive. To mitigate the need for manual annotation, recent
weakly-supervised approaches have been developed for sentence-level relation
extraction while limited work has been done on document-level relation
extraction. Weakly-supervised document-level relation extraction faces
significant challenges due to an imbalanced number "no relation" instances and
the failure of directly probing pretrained large language models for document
relation extraction. To address these challenges, we propose PromptRE, a novel
weakly-supervised document-level relation extraction method that combines
prompting-based techniques with data programming. Furthermore, PromptRE
incorporates the label distribution and entity types as prior knowledge to
improve the performance. By leveraging the strengths of both prompting and data
programming, PromptRE achieves improved performance in relation classification
and effectively handles the "no relation" problem. Experimental results on
ReDocRED, a benchmark dataset for document-level relation extraction,
demonstrate the superiority of PromptRE over baseline approaches.
