Title: Sensitivity-controlled event trigger identification in multi-level biomedical context

Authors: Chen Shen; Hongfei Lin; Zhengguang Li; Yonghe Chu; Zhihao Yang

Addresses: School of Computer Science and Technology, Dalian University of Technology, Dalian, Panjin, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, Panjin, China ' Software Technology Institute, Dalian Jiaotong University, Dalian, Liaoning, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, Panjin, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, Panjin, China

Abstract: The identification of biomedical event triggers serves as an important step in biomedical event extraction. It is a domain-specific task restricted to limited annotated text and language representations in computational models. To achieve a model that can learn and leverage more semantic information, most conventional methods rely on machine learning models, which require a series of artificially designed features. Moreover, existing methods have been conducted on imbalanced datasets, but have not adjusted for this. Therefore, we propose a novel framework to address imbalanced quantities of training data across biomedical event categories. This framework integrates convolutional and recurrent neural networks for better language representation, and leverages sensitivity-controlled support vector machine with an enhanced balanced loss function as the classifier of the network. The experiments conducted on the multi-level event extraction data set show that our approach provides a more balanced solution between precision and recall, and outperforms other state-of-the-art methods.

Keywords: event trigger identification; biomedical event extraction; imbalanced classification; SCSVM; sensitivity-controlled support vector machine; neural networks.

DOI: 10.1504/IJDMB.2020.112852

International Journal of Data Mining and Bioinformatics, 2020 Vol.24 No.3, pp.238 - 257

Received: 29 Apr 2020
Accepted: 14 Sep 2020

Published online: 21 Nov 2020 *

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