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

Authors: Jinyong Zhang; Dandan Fang; Weizhong Zhao; Jincai Yang; Wen Zou; Xingpeng Jiang; Tingting He

Addresses: School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China; Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin 541004, China ' School of Computer, Central China Normal University, Wuhan, China ' Division of Bioinformatics and Biostatistics, National Centre for Toxicological Research, Jefferson, AR 72079, USA ' School of Computer, Central China Normal University, Wuhan, China ' School of Computer, Central China Normal University, Wuhan, China

Abstract: 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.

Keywords: biomedical event trigger identification; end-to-end model; graph convolutional network; syntactic dependency tree; hierarchical attention mechanism; adaptive cost learning.

DOI: 10.1504/IJDMB.2020.107876

International Journal of Data Mining and Bioinformatics, 2020 Vol.23 No.3, pp.189 - 212

Received: 16 Mar 2020
Accepted: 18 Mar 2020
Published online: 26 Jun 2020 *

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