Piecewise convolutional neural networks with position attention and similar bag attention for distant supervision relation extraction

2021 ◽  
Author(s):  
Weijiang Li ◽  
Qing Wang ◽  
Jiekun Wu ◽  
Zhengtao Yu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Relation Extraction ◽  
Distant Supervision

scholarly journals Drug-Drug Interaction Extraction via Convolutional Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Shengyu Liu ◽  
Buzhou Tang ◽  
Qingcai Chen ◽  
Xiaolong Wang
Keyword(s):  
Neural Networks ◽  
Drug Interaction ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Relation Extraction ◽  
Good Choice ◽  
Support Vector ◽  
Vector Machines ◽  
Interaction Extraction ◽  
Drug Drug Interaction

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 anF-score of 69.75%, which outperforms the existing best performing method by 2.75%.

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