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

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com