ext-CRNN+Attention架构下的多类别文本信息分类

ext classification is a research hotspot in the field of data mining and information retrieval. In view of the current research process , traditional machine learning methods relies on manual feature extraction with high complexity; Deep learning network has strong feature expression ability, but the model is weak in interpretability, leading to the loss of key feature information. For this reason, the author designed the CRNN network in the way of network level combination, introduced Attention mechanism, and proposed a Text-CRNN+Attention model. Firstly, CNN was used to deal with the position invariance of local features and extracted efficient local feature information. Then, Attention mechanism was introduced to automatically weigh the output sequence information at each time to reduce the loss of key features when RNN was used to model the sequence features. The feature extraction in time and space is completed. The experimental results show that the accuracy of the proposed model is 2 to 3 percentage points higher than that of other models. When dealing with text data, the model not only guarantees the local correlation of data, but also strengthens the effective combination ability of sequence features.