计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 277-281.doi: 10.11896/j.issn.1002-137X.2019.08.046
方杰, 李培峰, 朱巧明
FANG Jie, LI Pei-feng, ZHU Qiao-ming
摘要: 事件同指消解是信息抽取的一项重要任务,在信息融合、问答系统、阅读理解中都有着重要的作用。文中提出了一种基于多种注意力机制的卷积神经网络的CorefNet方法,用于消解文档级事件同指。该方法通过深层卷积网络抽取事件特征,并使用多种注意力机制获取重要信息。相比过去大部分建立在概率模型和图模型上的传统方法,所提方法仅使用了少量特征;与目前主流的神经网络模型相比,文中方法可以提取深层的事件特征,明显提高了事件同指消解的准确率。在ACE2005数据集上的实验验证了CorefNet优于目前最优的基准系统。
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