# Semi-Supervised Convolutional Neural Network for Law Advice Online
## 1 Introduction
Deep learning methods have achieved  
Figure 1: The flowchart of the online law advice system. great success across several domains and tasks in the past few years [19; 20; 21; 22]. Empirical comparisons of our proposal with classical algorithms like naive Bayes, SVM, and KNN indicate the potential of deep learning-based methods as a powerful tool for text classification. Deep learning-based methods are an efficient version of neural networks, which can perform unsupervised [23], supervised [24], and semi-supervised learning [25]. However, these supervised learning models require a huge amount of labeled data to train thousands of attached parameters iteratively in the task domain, which makes it unfeasible in situations where sufficient amounts of manually-annotated data are not available or even do not exist. In addition, numerous annotated samples increase the labor cost and reduce the adaptability of the approach. Therefore, it raises the urgency of considering other alternatives to text classification. Here, we present a new semi-supervised model based on deep learning to solve the problem of data annotation. The deep neural network (DNN)-based semi-supervised method is one of the most promising approaches to implement text classification. As DNN-based methods developed, many intelligent approaches such as the deep belief network (DBN) [26], the recurrent neural network (RNN) [27; 28], the convolution neural network (CNN) [29], and other DNN-based approaches have been established. This paper introduces the application of CNN [30], which can effectively capture the internal feature of text data. CNN was originally designed for computer vision in order to consider feature information extraction and image classification as one task [31; 32; 33]. CNN is very successful in image processing, but its use in NLP is relatively new, which has some peculiarities. Text data are a one-dimensional representation compared with the image bi-dimensional representation. The convolution operation is designed as the temporal convolutions owing to this property. More work was done on using CNN to train text classifiers by representing natural language in a way similar to an image where each word has the representation [34]. This representation (namely embedding) is usually obtained through the application of a word vector model. This enabled a CNN to classify text with more high-level information. We note that a CNN can intuitively understand words from pixels, sentences from words, and more complex concepts from sentences. A CNN-based model with one layer was initially used for text processing. Based on this work, deeper architectures were proposed [35; 36]. The work in [37] was the first to present very deep CNN, which was applied to text classification. It is worth mentioning that CNN proved its ability to learn automatically from scratch using vector representations of text without prior knowledge of the language structure. As previously mentioned, CNN allows high-level understanding, provided the availability of sufficient data. As we all known, the CNN-based approach applies an amount of manually-annotated data, which increases the labor cost and reduces the adaptability of the approach [38]. Moreover, label data are difficult to obtain, which requires human experts for annotation. In fact, many machine-learning researchers find that unlabeled data can produce considerable improvement in learning performance [39]. Thus, a framework of semi-supervised learning is designed by small amounts of labeled data and large amounts of unlabeled data to learn effective feature representation for text classification [40]. When the number of label data is insufficient to train the parameters of the classifier, the paper [41] devised a boosting technique-based semi-supervised model, which can approximate the prior distribution of the classification functions. Our focus is on studying law text classification using CNN that emphasizes feature learning rather than manual feature engineering.