Chinese Sentence Classification Based on Convolutional Neural Network

2017 International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Gu, Chengwei^1 ; Wu, Ming^1 ; Zhang, Chuang^1

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Xitucheng Road No.10, Beijing, China^1

关键词: Activation functions;    Contextual information;    Convolutional neural network;    Convolutional Neural Networks (CNN);    Linear Support Vector Machines;    Neural network model;    Sentence classifications;    Softmax classifiers;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/261/1/012008/pdf
DOI  :  10.1088/1757-899X/261/1/012008

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.