用于文本情感极性分析的动态卷积神经网络超限学习算法

摘要

Aiming at improving the generalization performance of the dynamic convolutional neural network on text sentiment classification, a dynamic convolutional extreme learning machine algorithm was proposed. This algorithm modified the output layer of dynamic convolutional neural network by replacing the fully connection layer with the shallow random neural network. By utilizing the perturbation ability of the random generation of parameters, it is prone to mitigate the dependence on training samples and avoid over-fitting to improve the classification performance. Experiments on several public data sets show that this approach outperforms the dynamic convolutional neural network and extreme learning machine under the evaluation metrics including accuracy rate, F1-measure, etc.%为改善动态卷积神经网络在文本情感分类方法中的泛化能力，提出了一种动态卷积超限学习算法。对动态卷积神经网络的输出层加以改进，使用浅层随机神经网络替代全连接层，利用参数随机生成的扰动性能，降低分类端对训练样本的依赖，避免过拟合，提升分类性能。在公共数据集上的实验证明：相对改进前的动态卷积学习算法以及超限学习机，所提出的方法在准确率、F1测度等多个标准指标上均体现了更优的分类性能。