# Semi-Supervised Convolutional Neural Network for Law Advice Online
## 5 Performance Evaluation
### Dataset  
The law case description dataset, which was collected by relevant scholars, was derived from Find Law Network ([[http://china.findlaw.cn/](http://china.findlaw.cn/)]([http://china.findlaw.cn/](http://china.findlaw.cn/))) and China Law Document Network ([[http://www.falwwenshuwang.com/](http://www.falwwenshuwang.com/)]([http://www.falwwenshuwang.com/](http://www.falwwenshuwang.com/))), and we regarded it as the corpus to conduct the experiments. Aiming to conduct comparative experiments, we introduced the English standard dataset RCV1. Although the English standard dataset RCV1 is a corpus of Reuters news articles described in LYRL04 [60], it can be used to verify the performance of the proposed model. RCV1 includes 103 topic classifications, which belong to multi-label classification. In multi-label classification tasks, RCV1 shows good \begin{table}
\begin{tabular}{c c} \hline
**Experimental Environment** & **Environment Configuration** \\ \hline The operating system & Ubuntu 14.04 \\ CPU & i7-6700k \\ GPU & GTX 980 Ti \\ Internal storage & 64 GB \\ Programming language & Python \\ Deep learning framework & theano \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental environment and configuration. Figure 6: The frame diagram of automatic classification of law text. performance [61; 62]. Apart from Chinese word segmentation (CWS) techniques, all other techniques in Algorithm 1 were applied to the English standard dataset RCV1. The Chinese law case description dataset was regarded as the corpus to evaluate the model performance. The Chinese law case description dataset was associated with more than one law label, which belonged to multi-label classification. That is, a law case description text was entered into the law advice system, which was processed by the system, and then, multiple predefined law labels were recognized and assigned automatically that matched the event description. On the multi-label classification of 25 law categories, the entire corpus was divided into 16,000 items for the training set, 2000 items for the validation set, and 2000 items for the test set, as shown in Table 2. To use as unlabeled data, we chose 500 K law texts from the same data source, so that they were disjoint from the training set, validation set, and test set. There are many law categories in China. In this paper, we mainly deal with 25 categories including constitution, economic law, financial tax law, criminal law, medical law, road traffic law, civil law, ocean law, real estate and construction law, administrative law, labor law, civil service law, anti-corruption law, intellectual property law, individual income tax law, environmental law, press and publication law, judicial procedure law, labor education law, cultural relics protection law, radio and television policy law, tobacco law, religious law, industrial and commercial administration law, and information technology law. We set these 25 law categories as labels. Through the treatment of the law case description, infringed victim's rights or laws violated by the parties were initially confirmed. We noted that multiple rights of a victim may be violated or a party may violate multiple laws at the same time. For example, the party enters a law case description into the system, and after processing the system, a number of predefined legal labels such as economic law and financial tax law are output. That is to say, the party violated both economic law and financial tax law. The law advice system brings convenience for people who lack law knowledge. For preprocessing, firstly, we extracted high-frequency words from input data; secondly, adjacent characters were grouped into words based on Chinese word segmentation (CWS) techniques; thirdly, words were represented as vectors by the one-hot model.