Document-Level Relation Extraction with Attention Mechanisms
Named Entity Recognition(NER)is a vital task in document-level relation extraction.Traditional models in this domain construct entities by aggregating local mentions,thereby constraining entity representational capabili-ties.To address this limitation,this paper proposes a document-level relation extraction method that supplemented entity deficiencies through an attention mechanism.The approach concentrates on hierarchical features directed by predefined relations,and employs pooling to augment mention semantics.It introduces a cross-multi-head attention mechanism and residual connections for context-weighted processing,reinforcing associations among entities,con-text,and global information.Experiments on the DocRED dataset reveal improvements of 1.82%/1.73%and 1.81%/1.62%in the validation set F1/Ign_F1 and test set F1/Ign_F1,respectively.