Context Event Features and Event Embedding Enhanced Event Detection

Extracting valuable information from text has always been a hot point for research and event detection is an essential subtask of information extraction. Most existing methods of event detection only focus on sentence-level information and do not consider the correlation between different event types. To address these problems, in this paper, we propose a novel pre-trained language model based event detection framework named CFEE that utilizes document-level information and event correlation to enhance the event detection task. To obtain event correlation, we project all event types into a shared semantic space through a Skip-gram model, where the event correlation can be represented as the distance between event embeddings. In order to capture document-level information, we utilize a bidirectional recurrent neural network to fuse the context information. Experiments on the ACE2005 dataset demonstrate that our proposed model is better than most existing methods, and also demonstrate the effectiveness of event correlation and document-level information.