# Chinese Text Classification Model Based on Deep Learning
## 1 Introduction  
Motivated by the development of Internet technology and the progress of mobile social networking platforms, the amount of textual information is growing rapidly on the Internet. Given the powerful real-time nature of Internet platforms, great potential value is hidden in such textual information; however, effective organization and management is in high demand presently. Text classification, known as an effective method for text information organization and management, is widely employed in the fields of information sorting [1], personalized news recommendation, sentiment analysis [2], spam filtering, user intention analysis, etc.  
Machine-learning-based methods, including naive Bayes, support vector machine, and k-nearest neighbors, are generally adopted by traditional text classification. However, their performance depends mainly on the quality of hand-crafted features. Compared with the methods of machine learning, the method of deep learning proposed in 2006 is deemed as an effective method for feature extraction. Moreover, an increasing number of scholars have applied commonly used neural networks, including the convolutional neural network (CNN) and the recurrent neural network (RNN), to text classification.  
Among the two, RNN has attained remarkable achievement in handling serialization tasks. As RNN is equipped with recurrent network structure which can be used to maintain information, it can better integrate information in certain contexts. For the purpose of avoiding the problem of gradient exploding or vanishing in a standard RNN, long short-term memory (LSTM) [3] and other variants [4] have been designed for the improvement of remembering and memory accesses. Living upto expectations, LSTM does show a remarkable ability in the processing of natural language. Moreover, the other popular neural network, CNN, has also displayed a remarkable performance in computer vision [5], speech recognition, and natural language processing [6] because of its remarkable capability in capturing local correlations of spatial or temporal structures. In terms of natural language processing, CNN is able to extract n-gram features from different positions of a sentence through convolutional filters and then it learns both short- and long-range relations through the operations of pooling.  
LSTM [3] is good at dealing with serialization tasks but poor in the ability to extract features, performs well in extracting features but lacks the ability to learn sequential correlations. Therefore, in the paper, both the CNN and the specific RNN architecture--bidirectional long short-term memory (BLSTM)--are combined together to establish a new model named as the BLSTM-C model for text classification. The BLSTM is employed firstly to capture the long-term sentence dependencies and then CNN is adopted to extract features for sequence modeling tasks. In addition, our model is evaluated by applying it to Chinese text classification. The model is applied to both the English and Chinese language and then corresponding effects are compared with each other. It turns out that our model is more suitable for the Chinese language. Furthermore, it is also shown through our evaluation that our BLSTM-C model achieves remarkable results and it also outperforms a wide range of baseline models.