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Drug-Drug Interaction Extraction via Convolutional Neural Networks
Joint Authors
Wang, Xiaolong
Liu, Shengyu
Chen, Qingcai
Tang, Buzhou
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-01-31
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Drug-drug interaction (DDI) extraction as a typical relation extraction task in natural language processing (NLP) has always attracted great attention.
Most state-of-the-art DDI extraction systems are based on support vector machines (SVM) with a large number of manually defined features.
Recently, convolutional neural networks (CNN), a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks.
It is worth employing CNN for DDI extraction, which has never been investigated.
We proposed a CNN-based method for DDI extraction.
Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction.
The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.
American Psychological Association (APA)
Liu, Shengyu& Tang, Buzhou& Chen, Qingcai& Wang, Xiaolong. 2016. Drug-Drug Interaction Extraction via Convolutional Neural Networks. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100177
Modern Language Association (MLA)
Liu, Shengyu…[et al.]. Drug-Drug Interaction Extraction via Convolutional Neural Networks. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1100177
American Medical Association (AMA)
Liu, Shengyu& Tang, Buzhou& Chen, Qingcai& Wang, Xiaolong. Drug-Drug Interaction Extraction via Convolutional Neural Networks. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100177
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100177