Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
Zhaohui Yan, Songlin Yang, Wei Liu, Kewei Tu · 2023
Entity and Relation Extraction (ERE) is an important task in information extraction.Recent marker-based pipeline models achieve state-ofthe-art performance, but still suffer from the error propagation issue.Also, most of current ERE models do not take into account higherorder interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose Hyper-Graph neural network for ERE (HGERE), which is built upon the PL-marker (a state-of-the-art marker-based pipleline model).To alleviate error propagation,we use a high-recall pruner mechanism to transfer the burden of entity identification and labeling from the NER module to the joint module of our model.For higher-order modeling, we build a hypergraph, where nodes are entities (provided by the span pruner) and relations thereof, and hyperedges encode interactions between two different relations or between a relation and its associated subject and object entities.We then run a hypergraph neural network for higher-order inference by applying message passing over the built hypergraph.Experiments on three widely used benchmarks (ACE2004, ACE2005 and SciERC) for ERE task show significant improvements over the previous state-of-the-art PL-marker. 1