A tree-based neural network model for biomedical event trigger detection

Abstract

Biomedical event trigger detection is a heated research topic since its important role in biomedical event extraction. Previous studies show that syntactic features are very crucial for the task. However, existing methods largely focus on traditional statistical models, and usually capture syntactic features by extracting a set of manually-crafted features based on dependency tree. This limits the performance of the task. In this paper, we propose a tree-based neural network model, which can automatically learn syntactic features from dependency tree for trigger detection. Specifically, we use a recursive neural network to represent whole dependency tree globally, to better incorporate dependency-based syntax information. Results on MLEE and BioNLP-09 datasets show that the proposed model achieves 80.28% and 73.24% F1 score, respectively, outperforming traditional statistical models and neural baseline systems.