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.

我们提出了一种基于超图神经网络的 PL-marker 模型的 Entity and Relation Extraction (ERE) 方法，采用高召回修剪机制来减轻错误传播，并在建立的超图上运行超图神经网络进行高阶推理，实验结果显示在 ERE 任务上相较于之前最先进的 PL-marker 模型有明显的提升。