Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms
Yubo Chen, Hang Yang, Kang Liu, Jun Zhao, Yantao Jia · 2018
Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information.However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events.This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously.Firstly, we propose a hierarchical and bias tagging networks to detect multiple events in one sentence collectively.Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information.The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.