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.

翻译：实体与关系抽取（ERE）是信息抽取中的一项重要任务。近期基于标记的流水线模型达到了先进水平，但仍存在错误传播问题。此外，当前多数ERE模型未考虑多实体与多关系间的高阶交互作用，而高阶建模可能带来性能提升。本文提出面向ERE的超图神经网络方法（$\hgnn{}$），该模型基于PL-marker（一种先进的基于标记的流水线模型）构建。为缓解错误传播，我们采用高召回率剪枝机制，将实体识别与标注任务从命名实体识别模块转移至模型的联合模块。对于高阶建模，我们构建了一个超图，其中节点为实体（由跨度剪枝器提供）及其对应关系，超边则编码了两个不同关系之间或某关系与其关联的主语实体和宾语实体之间的交互。随后，我们通过在该超图上执行消息传递机制，利用超图神经网络进行高阶推理。在三个广泛使用的ERE任务基准数据集（\acef{}、\ace{}和\scierc{}）上的实验表明，本方法相较于前代先进模型PL-marker取得了显著性能提升。