Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of documents. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

中文翻译：

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具有多序关系的 Hypergraph 卷积网络，用于跨文档事件共指解析


识别文本中不同事件提及之间的共指关系（即事件共指解析）是自然语言处理中的一项重要任务。它有助于理解文本中各种事件之间的关联，并在信息提取、问答系统和阅读理解中起着重要作用。现有研究在提高事件共指解析的性能方面取得了进展，但也存在一些缺点。例如，现有的方法大多以串行处理模式分析文档中的事件数据，而不考虑事件之间的复杂关系，难以挖掘事件的深层语义。针对这些问题，本文提出了一种基于超图卷积神经网络的跨文档事件共指解析方法 （HGCN-ECR）。首先，使用 BiLSTM-CRF 模型标记从大量文档中提取的事件的语义角色;根据标注结果，确定事件的触发词和非触发词，并围绕事件触发词构建多文档事件超图。然后，使用超图卷积神经网络在多文档事件超图中学习高阶语义信息，并通过将每个事件关系视为一组单独的注意力机制，引入多头注意力机制来理解不同事件关系类型的隐藏特征。最后，采用前馈神经网络和平均链路聚类方法计算事件的共指得分并完成共指事件聚类，实现跨文档事件共指消解。 实验结果表明，跨文档事件共指解决方法优于基线模型。