# Semi-Supervised Convolutional Neural Network for Law Advice Online
## 6 Conclusions and Future Work  
A decision support online law consulting platform is necessary to automatically recognize and assign predefined law labels, which greatly reduces the workload of lawyers, while bringing convenience for people who lack law knowledge. In this paper, we proposed a new SSC algorithm for Chinese law text classification. Unlike English, there is no obvious division between Chinese words. Based on the Chinese word segmentation (CWS) techniques, adjacent characters were grouped into words in order to apply to the Chinese text classification tasks. Our method learned the embedding of small text regions from unlabeled data and then integrated the learned embedding into the supervised training. More specifically, the learned embedding regions with the two-view-embedding model were used as an additional input to the CNN's convolution layer to solve the problem of data annotation. In addition, to implement the multi-task learning, a multi-label classification algorithm was proposed, which assigned multiple categories to an instance. We conducted extensive experiments over a real dataset and compared our method with some state-of-the-art machine learning methods. On the Chinese data, the results suggested that in terms of precision, recall, F-1, and Hamming loss, our method respectively improved them by 7.76%, 7.86%, 9.19%, and 2.96% compared with the linear SVM. Moreover, compared to CNN, our method respectively improved by 4.46%, 5.76%, 5.14%, and 0.87% the precision, recall, F-1, and Hamming loss. It is worth mentioning that the robustness of this method made it suitable and effective for automatic classification of law text. Furthermore, the design concept proposed is promising, which can be utilized  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**RCV1**} & \multicolumn{4}{c}{**Chinese Law Case Description Dataset**} \\ \cline{2-9}  & **Precision** & **Recall** & **F-1** & **Hamming Loss** & **Precision** & **Recall** & **F-1** & **Hamming Loss** \\ \hline Linear SWM & 89.32\% & 84.54\% & 86.72\% & 9.38\% & 87.72\% & 84.12\% & 84.59\% & 10.88\% \\ CNN & 93.16\% & 88.24\% & 90.64\% & 7.96\% & 91.02\% & 86.22\% & 88.64\% & 8.79\% \\ Semi-supervised model & 95.37\% & 90.57\% & 92.87\% & 7.31\% & 92.83\% & 88.13\% & 90.43\% & 8.17\% \\ Our scheme (SSC) & 96.76\% & 93.16\% & 94.24\% & 6.34\% & 95.48\% & 91.98\% & 93.78\% & 7.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance in terms of precision, recall, F-1 score, and Hamming loss.  
in other real-world applications such as news classification and public opinion monitoring. However, there is still much work left to do. The extent to which this type of model can be scaled to much larger and wider domains remains an open question that we hope to pursue in our further work.  
All authors contributed equally to this work. [PERSON] and [PERSON] conceived and designed the experimental scheme; [PERSON] and [PERSON]. ([PERSON]) performed the experiments; [PERSON] and [PERSON]. ([PERSON]) contributed data analysis; [PERSON] and [PERSON] wrote the paper.  
This research was funded by Ministry of Education - China Mobile Research Fund MCM20180404; Chongqing Basic Research and Frontier Exploration Project cstc 2018 jcyjAX0167; Chongqing Artificial Intelligence Technology Innovation Major Theme Special Project cstc 2017 rgzn-zdyfX0035; Chongqing Key Industries Common Key Technological Innovation Specialized cstc 2017 zdcy-zdyfX0067.  
The authors declare no conflict of interest.