基于门控图卷积与动态依存池化的事件论元抽取  

Event Argument Extraction Using Gated Graph Convolution and Dynamic Dependency Pooling

作　　者：王士浩 王中卿[1] 李寿山[1] 周国栋[1] WANG Shi-hao;WANG Zhong-qing;LI Shou-shan;ZHOU Guo-dong(School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)

机构地区：[1]苏州大学计算机科学与技术学院,江苏苏州215006

出　　处：《计算机科学》2021年第S02期52-56,共5页Computer Science

基　　金：国家自然科学基金青年科学基金项目(61806137)。

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。Event argument extraction is a very challenging subtask of event extraction.This task aims to extract the arguments in the event and the role they played.It is found that the semantic features and dependency features of sentences play a very important role in event argument extraction,and the existing methods often don’t consider how to integrate them effectively.Therefore,this paper proposes an event argument extraction model using gated graph convolution and dynamic dependency pooling.This method uses BERT to extract the semantic features of sentences,and then two same graph convolution networks are used to extract the dependency features of sentences based on the dependency tree.The output of one graph convolution is used as the gating unit through the activation function.Then semantic features and dependency features are added and fused through gate unit.In addition,a dynamic dependency pooling layer is designed to pool the fused features.The experiment results on ACE2005 dataset show that the proposed model can effectively improve the performance of event argument extraction.

关 键 词：事件论元抽取 图卷积 依存句法特征 语义特征 门控机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]