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论文类型：期刊论文

发表时间：2021-03-05

发表刊物：INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS

卷号：24

期号：3

页面范围：238-257

ISSN号：1748-5673

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

摘要：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.