drug-drug interaction extraction via convolutional neural networks

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ID: 177785
2016
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Abstract
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%.
Reference Key
liu2016computationaldrug-drug Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Shengyu Liu;Buzhou Tang;Qingcai Chen;Xiaolong Wang
Journal advanced functional materials
Year 2016
DOI
10.1155/2016/6918381
URL
http://dx.doi.org/10.1155/2016/6918381
Keywords
medical informatics computer applications to medicine

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