Sensitivity-controlled event trigger identification in multi-level biomedical context Online publication date: Sun, 07-Feb-2021
by Chen Shen; Hongfei Lin; Zhengguang Li; Yonghe Chu; Zhihao Yang
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 24, No. 3, 2020
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.
Online publication date: Sun, 07-Feb-2021
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