摘要
针对卷积神经网络(CNN)中的池化操作会丢失部分特征信息和胶囊网络(CapsNet)分类精度不高的问题,提出了一种改进的CapsNet模型。首先,使用两层卷积层对特征信息进行局部特征提取;然后,使用CapsNet对文本的整体特征进行提取;最后,使用softmax分类器进行分类。在文本分类中,所提模型比CNN和CapsNet在分类精度上分别提高了3.42个百分点和2.14个百分点。实验结果表明,改进CapsNet模型更适用于文本分类。
In order to solve the problems that the pooling operation of Convolutional Neural Network(CNN)will lose some feature information and the classification accuracy of Capsule Network(CapsNet)is not high,an improved CapsNet model was proposed.Firstly,two convolution layers were used to extract local features of feature information.Then,the CapsNet was used to extract the overall features of text.Finally,the softmax classifier was used to perform the classification.Compared with CNN and CapsNet,the proposed model improves the classification accuracy by 3.42 percentage points and 2.14 percentage points respectively.The experimental results show that the improved CapsNet model is more suitable for text classification.
作者
尹春勇
何苗
YIN Chunyong;HE Miao(School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing Jiangsu 210044,China)
出处
《计算机应用》
CSCD
北大核心
2020年第9期2525-2530,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(61772282)。
关键词
文本分类
卷积神经网络
胶囊网络
动态路由
特征提取
text classification
Convolution Neural Network(CNN)
Capsule Network(CapsNet)
dynamic routing
feature extraction
