An end-to-end framework for biomedical event trigger identification with hierarchical attention and adaptive cost learning

As a prerequisite step in biomedical event extraction, event trigger identification has attracted growing attention in biomedical research. Existing approaches to biomedical event trigger identification have two major drawbacks: (1) each sentence in a biomedical document is handled separately, which ignores the global context; (2) they fail to treat the issue of imbalanced class which is induced by the sparseness of event triggers in biomedical documents. To improve the performance of biomedical event trigger identification, we propose a deep neural network-based framework which addresses effectively the two mentioned challenges accordingly. Specifically, the syntactic dependency tree and hierarchical attention mechanism are utilised to model both local and global contexts. Moreover, we propose an adaptive cost learning method to address the class imbalance issue in biomedical event trigger identification. Extensive experiments are conducted on two real-world data sets, and the results demonstrate the effectiveness of the proposed framework.