计算机科学 ›› 2019, Vol. 46 ›› Issue (8): 244-248.doi: 10.11896/j.issn.1002-137X.2019.08.040
张义杰, 李培峰, 朱巧明
ZHANG Yi-jie, LI Pei-feng, ZHU Qiao-ming
摘要: 事件时序关系分类是事件抽取的重要后续任务。随着深度学习技术的发展,神经网络在事件时序关系分类任务中发挥着重要作用。但是,对于传统的循环神经网络或卷积神经网络而言,处理结构信息和捕获长距离依赖关系仍然是一个重大挑战。针对这个问题,文中提出了一种基于自注意力机制的事件时序关系分类模型架构,它可以直接捕获句子中任意两个词例之间的关系。将该机制与非线性网络层结合,可以使事件时序关系分类的性能得到显著提高。在TimeBank-Dense和Richer Event Description数据集上的对比实验证明:所提方法优于现有的大多数神经网络方法。
中图分类号:
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