计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 52-56.doi: 10.11896/jsjkx.201200259
王士浩, 王中卿, 李寿山, 周国栋
WANG Shi-hao, WANG Zhong-qing, LI Shou-shan, ZHOU Guo-dong
摘要: 事件论元抽取是事件抽取任务中一个极具挑战性的子任务。该任务旨在抽取事件中的论元及论元扮演的角色。研究发现,句子的语义特征和依存句法特征对事件论元抽取都有着非常重要的作用,现有的方法往往未考虑如何将两种特征有效地融合起来。因此,提出一种基于门控图卷积与动态依存池化的事件论元抽取模型。该方法使用BERT抽取出句子的语义特征;然后通过依存句法树设计两个相同的图卷积网络,抽取句子的依存句法特征,其中一个图卷积的输出会通过激活函数作为门控单元;接着,语义特征和依存句法特征通过门控单元后相加融合。此外,还设计了一个动态依存池化层对融合后的特征进行池化。在ACE2005数据集上的实验结果表明,该模型可以有效地提升事件论元抽取效果。
中图分类号:
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