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%.

scholarly journals Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 688
Author(s):  
Adrián Vázquez-Romero ◽  
Ascensión Gallardo-Antolín
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Final Decision ◽  
Experimental Protocol ◽  
Ensemble Averaging ◽  
One Dimensional ◽  
Vector Machines ◽  
Detection Of Depression

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.

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