Drug-Drug Interaction Extraction via Convolutional Neural Networks

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,...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2016-01, Vol.2016 (2016), p.1-8
Main Authors: Wang, Xiaolong, Chen, Qingcai, Tang, Buzhou, Liu, Shengyu
Format: Article
Language:English
Subjects:
Algorithms
Data Mining - methods
Drug Interactions
Humans
Machine Learning
Models, Statistical
Natural Language Processing
Neural Networks (Computer)
Pattern Recognition, Automated - methods
Regression Analysis
Semantics
Support Vector Machine
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Summary: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%.
ISSN:1748-670X
1748-6718
DOI:10.1155/2016/6918381