Event trigger identification for biomedical events extraction using domain knowledge


Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.

Publication DOI: https://doi.org/10.1093/bioinformatics/btu061
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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Additional Information: This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of recordZhou, D., Zhong, D., & He, Y. (2014). Event trigger identification for biomedical events extraction using domain knowledge. Bioinformatics, 30(11), 1587-1594 is available online at: http://dx.doi.org/10.1093/bioinformatics/btu061 Supplementary data are available at Bioinformatics online.
Uncontrolled Keywords: Biochemistry,Molecular Biology,Computational Theory and Mathematics,Computer Science Applications,Computational Mathematics,Statistics and Probability,General Medicine
Publication ISSN: 1367-4803
Last Modified: 27 Jun 2024 08:29
Date Deposited: 11 May 2015 14:45
Full Text Link: http://bioinfor ... tent/30/11/1587
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2014-06-01
Published Online Date: 2014-01-30
Authors: Zhou, Deyu
Zhong, Dayou
He, Yulan (ORCID Profile 0000-0003-3948-5845)



Version: Accepted Version

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