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Indexed by:Journal Papers

Date of Publication:2020-03-01

Journal:IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

Included Journals:SCIE

Volume:17

Issue:2

Page Number:599-607

ISSN No.:1545-5963

Key Words:Biological system modeling; Biomedical event extraction; convolutional neural networks; dependency word embeddings; rectified linear unit

Abstract:Biomedical event extraction is important for medical research and disease prevention, which has attracted much attention in recent years. Traditionally, most of the state-of-the-art systems have been based on shallow machine learning methods, which require many complex, hand-designed features. In addition, the words encoded by one-hot are unable to represent semantic information. Therefore, we utilize dependency-based embeddings to represent words semantically and syntactically. Then, we propose a parallel multi-pooling convolutional neural network (PMCNN) model to capture the compositional semantic features of sentences. Furthermore, we employ a rectified linear unit, which creates sparse representations with true zeros, and which is adapted to the biomedical event extraction, as a nonlinear function in PMCNN architecture. The experimental results from MLEE dataset show that our approach achieves an F1 score of 80.27 percent in trigger identification and an F1 score of 59.65 percent in biomedical event extraction, which performs better than other state-of-the-art methods.