# Semi-Supervised Convolutional Neural Network for Law Advice Online
## 1 Introduction  
With the progress of information technology and the rapid development of Web2.0 network technology, the incredible development of the Internet has changed people's lives, entertainment, and consumption pattern greatly. At present, as more and more users are willing to carry out online law advice, law case handling, and other services, several kinds of law service patterns were shown in [1; 2]. How to find useful information quickly and accurately from law case description becomes very important for law text classification [3; 4]. An decision support online law consulting platform isnecessary to automatically recognize and assign the predefined law class, as shown in Figure 1. Firstly, a user enters text into the system for data preprocessing. Secondly, the processed data are input to the semi-supervised model for semantic analysis. Then, the semi-supervised model automatically recognizes and assigns the predefined law label. Finally, the user gets the information that he/she needs on the consulting system. We note that the system greatly reduces the workload of lawyers and brings convenience for people who lack law knowledge. Natural language processing (NLP) is one of the representative areas whose purpose is to make the computer correctly handle natural language and then make an appropriate response [5]. In the field of NLP [6; 7], text classification is an important component, which has become a research focus [8; 9] and achieved a breakthrough in many life scenarios. Some commonly-used methods have been used for text classification, the two most typical types of which are statistical methods [10] and semantic analysis methods [11]. The main statistics-based classification algorithms are naive Bayes [12], support vector machine (SVM) [13], and K-nearest-neighbor (KNN) [14]. Now, we specifically talk about these three types of algorithms based on statistical methods. Firstly, learning a naive Bayesian classifier from data is an important technique for data classification [15]. In spite of its simplicity, the naive Bayesian classifier can perform surprisingly well in many domains. However, the naive Bayesian classifier violates the independence assumption, which assumes that the properties are independent of each other. The assumption is often not true in practical applications, which has some impact on the naive Bayesian classification. Then, SVM [16], being recognized as one of the most useful kernel-based tools for data classification, is widely used in real-world problems. To implement the structural risk minimization, SVM constructs two parallel optimal support hyperplanes, which maximize the margin between two classes. However, SVM needs to solve the quadratic programming problem, whose computational complexity is \(\mathsf{O}\left(m^{3}\right)\) where \(m\) is the scale of the dataset. Moreover, the time-consuming training stage of SVM may restrict its application to many real-world problems. In pattern recognition, KNN is a nonparametric learning algorithm that is used for the classification task [17]. Owing to the simple implementation and the high classification efficiency, it is commonly used as a standard classifier model to evaluate the performance of different feature selection algorithms [18]. In order to predict the label of a new instance, \(k\) closest neighbors, which are obtained according to the distance metric, are found first, and then, the dominant label among the \(k\) neighbors is assigned to the new instance. In addition, the class label of a new instance is determined by its closest neighbor when \(k=1\). However, when the data size is too large, the computational efficiency of the algorithm is affected greatly. To address these above-mentioned impediments, a simple, but effective deep learning technique based on semantic analysis methods is proposed for text classification. Deep learning-based methods are able to capture both the local and the global textual semantics to model high-order label correlations, having a tractable computational complexity at the same time.