Multi-label News Text Classification Based on AlBERT-TextCNN Model
Aiming at the multi-label news text classification task of intelligent information push managers,a solution based on ALBERT-CNN model is proposed.The ALBERT pre-trained model and TextCNN Convolutional Neural Network are employed to comprehensively understand semantics and extract features.Semantic filtering is performed through the ALBERT model to accurately grasp the content and themes of news texts,which are then passed to the TextCNN model for classification and label prediction.The sigmoid function is utilized to output the probability of each label,achieving precise multi-label classification.The experiment verifies 382 688 data from the Toutiao client.The F1-Score of ALBERT-CNN model reaches 92.05%,the Recall reaches 96.8%,and the Precision reaches 90%.Compared with the traditional ALBERT and ALBERT-Dense models,it has improved in F1-Score and Recall.It is slightly lower than ALBERT-Dense model in Precision.This study provides a new solution for enhancing information push efficiency and reducing the spread of misleading information.
万方数据
维普
