Document-Level Relation Extraction with Global and Path Dependencies

Document-level relation extraction (RE) focuses on extracting relations for each entity pair in the same sentence or across different sentences of a document. Several existing methodologies aim to capture the intricate interactions among entities across a document by constructing diverse document graphs. However, these graphs frequently cannot sufficiently model the intricate global interactions and concurrent explicit path reasoning. Therefore, we introduce a distinctive graph-based model designed to assimilate global and path dependencies within a document for document-level RE, termed graph-based global and path dependencies (GGP). Specifically, the global dependency component captures interactions between mentions, entities, sentences and, the document through two interconnected graphs: the mention-level graph and the entity-level graph (ELG). To integrate relevant paths essential for the designated entity pair, the path dependency component consolidates information from various multi-hop paths of the target entity pair through an attention mechanism on the ELG. In addition, we devised an innovative method for learning path representation, which encapsulates relations and intermediate entities within the multi-hop path in the ELG. Comprehensive experiments conducted on standard document-level RE and CDR datasets reveal the following key findings: (i) GGP achieves an Ign F1 score of 59.98%, surpassing baselines by 0.61% on the test set; and (ii) the integration of various features derived from entities, sentences, documents, and paths enhances GGP's performance in document-level RE.