基于BiLSTM_ATT_CNN中文专利文本分类

随着大数据和人工智能的发展, 将人工处理专利的方式转换为自动化处理成为可能. 本文结合卷积神经网络(CNN)提取局部特征和双向长短记忆神经网络(BiLSTM)序列化提取全局特征的优势, 在BiLSTM隐藏层引入注意力机制(Attention机制), 提出了针对中文专利文本数据的BiLSTM_ATT_CNN组合模型. 通过设计多组对比实验, 验证了BiLSTM_ATT_CNN组合模型提升了中文专利文本分类的准确率.

Chinese Patent Text Classification Based on BiLSTM_ATT _CNN Model

With the development of big data and artificial intelligence, it is possible to transform the manual processing of patents into automated processing. In this study, combined with the advantages of Convolutional Neural Network (CNN) to extract local features and Two-way Long and Short Term Memory neural network (BiLSTM) to serialize and extract global features, the attention mechanism is introduced in the hidden layer of BiLSTM, and a BiLSTM_ATT_CNN combination model for Chinese patent text data is proposed. The BiLSTM_ATT_CNN combined model improves the accuracy of Chinese patent text classification by designing multiple comparison experiments.