Title: Biomedical event trigger detection based on convolutional neural network

Authors: Jian Wang; Honglei Li; Yuan An; Hongfei Lin; Zhihao Yang

Addresses: School of Computer Science and Technology, Dalian University of Technology, Dalian, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, China ' College of Computing & Informatics, Drexel University, Philadelphia, PA, USA ' School of Computer Science and Technology, Dalian University of Technology, Dalian, China ' School of Computer Science and Technology, Dalian University of Technology, Dalian, China

Abstract: Event trigger detection, which plays a key role in biomedical event extraction, has attracted significant attention recently. However, most approaches are based on statistical models, much relying on complex hand-designed features. In this paper, we utilise the ability of Convolutional Neural Network (CNN) for addressing higher-level features automatically to explore correlations between a trigger and an event type. We only keep one candidate trigger along with N-words around it and entity mention features as a raw input, giving up complex input with hand-designed features that derived from currently existed Natural Language Processing (NLP) tools. Our experiments on Multi-Level Event Extraction (MLEE) corpus showed that the method achieved a higher F-score of 78.67% compared to the state-of-the-art approaches. The results demonstrate that the proposed method is effective for event trigger detection.

Keywords: biomedical event extraction; event trigger detection; convolutional neural networks; word representation; biomedical events; natural language processing; NLP.

DOI: 10.1504/IJDMB.2016.077067

International Journal of Data Mining and Bioinformatics, 2016 Vol.15 No.3, pp.195 - 213

Received: 07 Dec 2015
Accepted: 29 Dec 2015
Published online: 20 Jun 2016 *

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