# Chinese Text Classification Model Based on Deep Learning
## 2 Related Work  
It is widely acknowledged that deep-learning-based neural network models have achieved great success in natural language processing. This paper focuses on establishing a new model that is able to obtain better results in the classification of Chinese text. To ensure that the computer can understand human language, the first step of text classification usually goes to representing the text with vectors which will later feed into the neural network. Therefore, the quality of the representation is doomed to play a quite significant role in the classification. For the final aim of obtaining a better representation of text, TF-IDF (Term Frequency-Inverse Document Frequency) and bag-of-words were employed in early research. Bag-of-words treats texts as unordered sets of words and each word of them is represented by a one-hot vector, a sparse vector in the size of the vocabulary, with 1 in the entry representing the word and 0 in all other entries [1]. Accordingly, this vector loses both the word order and syntactic feature. [PERSON] came up with the idea of distributed representations of words and paragraphs, and it is shown by relevant experiments that word and paragraph vectors outperform bag-of-words models as well as other techniques for text representations [7; 8]. In many works on text representation learning published recently, it is known that there are generally two popular neural network models that had achieved their remarkable performance--CNN and RNN. The ability of CNNs [5] in extracting features from inputs, such as images, is outstanding and it has also achieved remarkable result in image classification. In such process, 2D convolution and 2D pooling operation are usually used to represent image input [5; 9]. As for text input, [PERSON] utilized 1D convolution to perform feature mapping and then applied 1D max pooling operation over the time-step dimension to obtain a fixed-length output [6; 10]. Moreover, in 2017, [PERSON] et al. adopted a very deep CNN in the tasks of text classification by pushing the depth to 29 convolutional layers [11]. RNN, as what is indicated by its name, is known as a kind of neural network that is equipped with a recurrent structure, for which RNN is capable of preserving sequence information over time. In addition, this feature enables that RNNs is applicable for serialization tasks such as text classification and speech recognition. Furthermore, [PERSON] et al. [12] proposed a tree-LSTM, a variant of RNN allowing for richer network topologies where each LSTM unit is able to incorporate information collected from multiple child units. In addition, [PERSON] et al. [13] achieved success in extracting meaningful features from documents automatically by combining bi-directional LSTM with an attention mechanism. Our work focuses mainly on Chinese text classification, which is known as a completely different language from English. In English, words are generally separated by spaces and an independent meaning is available for each word, while Chinese words, on the contrary, have no spaces to separate them. In order to solve this problem, [PERSON] et al. [14] put forward an HHMM-based Chinese lexical analyzer, ICTCLAS, which actually showed the effectiveness of class-based segmentation HMM(Hidden Markov Model). Furthermore, [PERSON] et al. employed a deep learning method--LSTM--to conduct Chinese word segmentation and achieved better accuracy in many popular datasets in comparison with the models based on more complex neural network architectures. As what is stated above, LSTM performs poorly in extracting features, while CNN lacks the ability to learn sequential correlations. In this paper, a new model integrating BLSTM with CNN is established for Chinese news classification. There has already been research that focuses on computer vision tasks such as image captions by combining CNN with LSTM. Moreover, [PERSON] et al. [15] also proposed a C-LSTM model that integrates CNN with an LSTM network for sentence modeling.