Entity relation extraction based on candidate relation attention mechanism and interaction graph

A fundamental task in information extraction is Relation Extraction (RE). However, existing methods often struggle with overlapping relations and nested entity extraction, and the modeling of entity-relation interactions is weak. In order to tackle these challenges, this study presents the CraRel model, an entity-relation extraction model based on candidate relation attention mechanisms and interaction graphs. By enhancing the interaction between entities and relations, the model effectively tackles overlapping relations and nested entities, improving the accuracy and robustness of relation extraction. The experimental findings indicate that this approach surpasses currently popular models across various public datasets.