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The challenges faced by text classification with large tag systems in naturallanguage processing tasks include multiple tag systems, uneven datadistribution, and high noise. To address these problems, the ESimCSEunsupervised comparative learning and UDA semi-supervised comparative learningmodels are combined through the use of joint training techniques in themodels.The ESimCSE model efficiently learns text vector representations usingunlabeled data to achieve better classification results, while UDA is trainedusing unlabeled data through semi-supervised learning methods to improve theprediction performance of the models and stability, and further improve thegeneralization ability of the model. In addition, adversarial trainingtechniques FGM and PGD are used in the model training process to improve therobustness and reliability of the model. The experimental results show thatthere is an 8% and 10% accuracy improvement relative to Baseline on the publicdataset Ruesters as well as on the operational dataset, respectively, and a 15%improvement in manual validation accuracy can be achieved on the operationaldataset, indicating that the method is effective.

ESimCSE Unsupervised Contrastive Learning Jointly with UDA Semi-Supervised Learning for Large Label System Text Classification Mode