
EnriCo, a novel framework, leverages attention mechanisms to foster rich and expressive representations of entities and relations, while incorporating task-specific and dataset-specific constraints during decoding to promote structured and coherent outputs.


coremsg

enriched-representation-and-globally-constrained-inference-for-accurate-joint-entity-and-relation-extraction


Enriched Representation and Globally Constrained Inference for Accurate Joint Entity and Relation Extraction


title_rewrite


GraphER, a novel approach to information extraction, formulates the task as graph structure learning to enhance the model's ability to dynamically refine and optimize the graph structure during the extraction process, enabling better interaction and structure-informed decisions for entity and relation prediction.


a-structure-aware-text-to-graph-model-for-joint-entity-and-relation-extraction


A Structure-aware Text-to-Graph Model for Joint Entity and Relation Extraction
