基于事件模式及类型的事件检测模型

针对触发词定义标准模糊、语料标注成本高等问题，提出一种基于事件模式及类型的事件检测深度学习模型(PTNN)。首先基于实体的语法及语义特征获取潜在论元；其次将潜在论元抽象为角色，结合语法、语义、角色特征构建嵌入表示，增强输入对事件模式的体现；最后利用Bi-LSTM和基于事件类型的注意力机制，完成事件及类型判定。模型在不识别触发词的前提下，通过强化事件模式特征实现事件检测，避免了触发词标注困难的问题，证明了事件模式在神经网络上对事件检测的积极作用，将同类方法的最优效果提升了3%，且达到了基于触发词的检测效果。

Event Detection Model Based on Event Pattern and Type Bias

To address the problems of vague criteria for trigger word definition and the high cost of corpus annotation, a deep learning model for event detection called pattern and type based neural network (PTNN) is proposed. First, potential theorems are obtained based on entities' syntactic and semantic features. Then, the potential theorems are abstracted as roles. The embedding representation of PTNN is constructed by combining syntactic, semantic, and role features to enhance the representation of event patterns. Last, event detection and type determination are accomplished by using Bi-LSTM (bidirectional long short-term memory) with an event type-based attention mechanism. The model achieves event detection by enhancing event pattern features instead of identifying trigger words, thus avoiding the challenging problem of trigger word annotation. Such an approach demonstrates the positive effect of event patterns for event detection on neural networks. Experiments demonstrate that it improves the state-of-the-art of event detection by 3%.