【论文笔记】Self-regulation: Employing a Generative Adversarial Network to Improve Event Detection

Introduction

Challenge 1:常见词、歧义词和代词在事件中的频繁使用使它们更难被发现

Generality – taken home <Transport>
Ambiguty 1 – campaign in Iraq <Attack>
Ambiguty 2 – political campaign <Elect>
Coreference – Either its bad or good <Marry>

Challenge 2:基于神经网络的方法受到更多来自虚假特征的影响，在这里，虚假特征被指定为与事件在语义上类似的潜在信息，但实际上并非如此

Prison authorities have given the nod for Anwar to be taken home later in the afternoon.
Trigger: taken. Event Type: Transport

实验设置与结果

数据集：ACE2005，TAC-KBP2015

实验结果

1. 触发器识别
   
2. 事件分类
   
3. embedding类型
   
4. 适应性