## 1 Introduction
[19] optimized and improved the fundamental formula in NB algorithm and come up with CIT-NB algorithm, which combined the core ideas of category and the improved Term Frequency-Inverse Document Frequency (TF-IDF) algorithm. [PERSON] et al. [23] proposed a SVM method based on semi-supervised learning for short text classification. By using TF-IDF as the weighting function, and using semi-supervised learning algorithm for iterative training, this method can improve the performance of traditional SVM method. Among these methods, SVM achieves better results than other models in the light of short text classification [11; 24]. With the help of kernel learning, the classification performance of SVM can be further improved [10]. However, the selection of kernel functions has great influence on the classification performance. Therefore, different kernel functions may lead to different classification results, and a single kernel function is not applicable to all types of short text classification. Relevant studies have shown that the compound kernel generated by multi-kernel learning is not always superior to the base kernel, which may attribute to shallow network structure of multi-kernel learning. In other words, multi-kernel learning has limitations to some extent, and is not competent enough to express relatively complicated compound kernel via linear or nonlinear combination of the base kernels [25]. Recently, models based on deep learning have attracted more attention [26]. Deep learning is an important branch in the field of machine learning.The concept of deep learning was first proposed by [PERSON] et al., whose research on artificial neural network was published in _Science_ in 2006 [14]. Now, as a prevalent network at present, the convolutional neural network has attained satisfying results in many fields due to its powerful ability of feature extraction [27; 28]. In the light of text classification, [PERSON] applied the convolutional neural network to classify short film criticism [16]. [PERSON] et al. introduced the attention mechanism to CNN and successfully applied it to document modeling [29]. Besides the multi-layer perceptron and CNN, another neural network structure with recurrent unit namedRecurrent Neural Network (RNN) has attracted more attention on text classification [30; 31]. In fact, a piece of short text is actually a short sequence in which the occurrence of each word is correlated with its context. RNN is a kind of neural networks which considering both the current input and the previous information for generating the current output. Therefore, comparing with multi-layer perceptron and CNN, RNN is more popular for processing sequence data. However, one shortcoming of the conventional RNN is that they just make use of the previous context, the future context is still not exploited [32]. To overcome the limitations, [PERSON] et. al [33] proposed a bidirectional recurrent neural network (BRNN). Given a specific time frame, BRNN can take into account all available input information in the past and future, therefore it can effectively retain the context features of the sequence data, such as text data. To solve the kernel learning problem existing in SVM, and considering the mapping ability of deep neural networks, [PERSON] et al. [34] introduce a short text classification method based on DNMSVM. It is able to explicitly express the kernel with a multilayer perceptron, and map the original text data to an appropriate dimensional space. Meanwhile, SVM is applied for classification in that appropriately mapped dimensional space. Theoretically, DNMSVM can approximate any kernel function. However, as mentioned above, there are various neural network structures besides multi-layer perceptron, and the BRNN has more advantages on sequence data processing.