关键词: 文本分类, 文本特征提取, 深度学习, 半监督学习

Abstract: As an important content of judicial information disclosure，case documents should be disclosed to the public after the trial． Some case documents involving juvenile are likely to cause the disclosure of juvenile personal privacy information． In order to conduct targeted privacy protection processing，the first step is to accurately identify documents involving juvenile information from a large number of case documents． At the same time，in order to solve the problem of difficulty in effective supervised learning due to the lack of labeled samples in the real data set，this paper proposed a juvenile case documents recognition method based on semi-supervised learning． Firstly， the corpus text of the case document was pre-processed，and then the features of the text were extracted with Word2Vec and BEＲT-wwm-ext． After the above processing，the long corpus text was converted into the data format that can be used as the input for the classification model． Then the classification model was trained with the PU learning method，and an effective classifier was constructed with a large number of unlabeled samples under the condition of few positive examples． Then，based on the prediction results of the classification model，active learning method was employed to obtain keywords and screen the prediction results，so as to further improve the prediction effect． Finally，the case documents recognition method proposed in this article achieves a recall of 98. 67% and a precision of 81. 02% on the test set constructed based on the proportion of real scenes．

Key words: text classification, text feature extraction, deep learning, semi-supervised learning