计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 292-299.doi: 10.11896/jsjkx.211200108
曹金娟, 钱忠, 李培峰
CAO Jinjuan, QIAN Zhong, LI Peifeng
摘要: 事件可信度是对文本中事件真实情况的一种描述,是自然语言处理领域许多相关应用的基本任务。目前,大多数关于事件可信度的相关研究都是使用标注的事件进行事件可信度识别,不方便实际应用,并且忽略了不同事件源对事件可信度的影响。针对现有问题,提出了一个端到端的事件可信度识别的联合模型JESF。该模型可以同时进行事件识别、事件源识别、事件可信度识别3个任务;使用BERT(Bidirectional Encoder Representations from Transformers)和语言学特征加强单词的语义表示;使用注意力机制(Attention)和依存句法树构建图卷积神经网络(Graph Convolutional Network,GCN),以有效地提取语义和句法特征。特别地,该模型也可以应用于只考虑默认源(文本作者)的事件可信度任务。在FactBank,Meantime,UW,UDS-IH2等语料上的实验结果显示,所提模型优于基准模型。
中图分类号:
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