关键词: 文本分类, 全卷积神经网络, 字符级, 局部响应归一化层（LRN）, 特征提取

Abstract:

In traditional convolution neural network text classification model, too many parameters in the fully connected layer can cause the over-fitting problem. Therefore, this paper introduces the idea of fully convolution in image segmentation into character-level text classification task for the first time, which not only avoids the over-fitting problem, but also reduces the number of parameters by replacing fully connected layers with convolutional layers, thus speeding up the convergence speed of the model. In addition, the processing of words and phrases in the text classification is insufficient to obtain text information. This paper uses a character-level fully convolutional neural network to obtain text information sufficiently. It adds the local response normalization layer to improve the performance of the model. The experimental part uses multiple indicators to evaluate the model, which fully proves the validation of the model. Compared with other models, the proposed model performs better in two classification and multi classification tasks.

Key words: text classification, fully convolutional neural network, character level, Local Response Normalization（LRN）, feature extraction