计算机科学 ›› 2021, Vol. 48 ›› Issue (7): 292-298.doi: 10.11896/jsjkx.200500133
程思伟1, 葛唯益2, 王羽2, 徐建1
CHENG Si-wei1, GE Wei-yi2, WANG Yu2, XU Jian1
摘要： 触发词检测是事件抽取的一项基本任务,该任务涉及对触发词进行识别和分类。目前,已有工作主要存在两方面的问题:1)用于触发词检测的神经网络模型只考虑了句子的顺序表示,且通过顺序建模的方法在捕捉长距离依赖关系时效率较低;2)基于表示的方法虽然解决了手动提取特征的问题,但用作初始训练特征的词向量对句子的表示程度有所欠缺,难以捕捉深层的双向表征。因此,文中提出了一种基于BERT模型和GCN网络的触发词检测模型BGCN,该模型通过引入BERT词向量来强化特征表示,并引入句法结构来捕捉长距离依赖,对事件触发词进行检测。实验结果表明,所提方法在ACE2005数据集上的表现优于其他现有的神经网络模型。
中图分类号:
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