Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks

TL;DR

This paper introduces a hypergraph neural network model for entity and relation extraction that reduces error propagation and captures higher-order interactions, leading to improved performance on standard benchmarks.

Contribution

It proposes a novel hypergraph neural network approach built on a marker-based pipeline, incorporating span pruning and higher-order inference for better ERE accuracy.

Findings

Significant performance improvements over state-of-the-art models.

Effective higher-order interaction modeling via hypergraphs.

Reduced error propagation through span pruning.

Abstract

Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeling could be beneficial.In this work, we propose HyperGraph neural network for ERE ($\hgnn{}$), 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 (\acef{}, \ace{} and \scierc{}) for ERE task show significant improvements over the previous state-of-the-art PL-marker.