Chinese causal event extraction using causality‐associated graph neural network
Chinese causal event extraction using causality‐associated graph neural network
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DOI：
10.1002/cpe.6572
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Jianqi Gao;Xiangfeng Luo;Hao Wang
中科院分区：
文献类型：
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作者：
Jianqi Gao;Xiangfeng Luo;Hao Wang
因果事件提取（CEE）旨在从文本中识别和提取因果事件对，这是自然语言处理中的一项基本任务。最近的研究将 CEE 视为序列标记问题。然而，语言的复杂性和文本描述的模糊性导致提取器的准确性较低。为了解决上述问题，考虑先验知识，例如基于因果指标构建的因果网络，可以表示因果之间的信息转换，可能对CEE有所帮助。在本文中，我们提出因果关联图神经网络，通过考虑重要的因果词来整合领域内知识。外部因果知识被建模为因果关联图（CAG）。然后，我们根据从 CAG 获得的关系，使用图神经网络 (GNN) 捕获句子中事件内提及和事件间因果关系的复杂关系。最后，句子序列和 GNN 嵌入的先验因果知识被输入到多尺度卷积和双向长短期记忆网络中。两个数据集的实验结果表明，我们的方法优于最先进的基线。
Causal event extraction (CEE) aims to identify and extract cause‐effect event pairs from texts, which is a fundamental task in natural language processing. Recent research treat CEE as a sequence labeling problem. However, the linguistic complexity and ambiguity of textual description results in the low accuracy of extractors. To address the above issues, considering the prior knowledge like the causal network constructed based on the causal indicators, which can represent information transition between cause and effect, may helpful for CEE. In this article, we propose causality‐associated graph neural network to incorporate in‐domain knowledge by taking important causal words into account. External causal knowledge is modeled as causal associated graph (CAG). Then we use graph neural networks (GNN) to capture the complex relationship of intraevent mentions and interevent causality in a sentence based on the relationship obtained from CAG. Finally, sentence sequence and prior causal knowledge of GNN embedding are fed into multiscaled convolution and bidirectional long short‐term memory networks. Experimental results on two datasets show that our method outperforms the state‐of‐the‐art baseline.