Globally normalized neural model for joint entity and event extraction

Abstract

Extracting events from texts using neural networks has gained increasing research focus in recent years. However, existing methods prepare candidate arguments in a separate classifier suffering from the error propagation problem and fail to model correlations between entity mentions and event structures. To improve the performance of both entity recognition and event extraction, we propose a transition-based joint neural model for the tasks by converting graph structures to a set of transition actions. In particular, we design ten types of novel actions and introduce a global normalization strategy to alleviate the label bias issue. We conduct experiments based on the widely used Automatic Content Extraction (ACE) corpora and the results show that our model achieves 88.7% F1-score on entities and 75.3% F1-score on event triggers, outperforming the baseline neural networks by a large margin. Further in-depth analysis shows the effectiveness of our model in capturing structural dependencies in long sentences. The proposed model can be used for facilitating a range of downstream tasks.