关键词: 多标签文本分类, 电力客服工单, 对比学习, 特征增强, 预训练语言模型

Abstract: Multi-label classification of electric power customer service work order texts was demonstrated to be crucial for enhancing service efficiency and user satisfaction. To mitigate the insufficient modeling of label relationships and class imbalance in multi-label text classification of electric power customer service work orders, a multi-label text classification method integrating feature enhancement and contrastive learning was proposed. First, the text features of customer service work orders were extracted by employing a pre-trained language model. Then, an innovative text feature enhancement method was developed by integrating global encoding with multi-head attention mechanisms and local encoding modules based on convolutional neural networks. Finally, a K-Nearest Neighbors (KNN) framework enhanced with contrastive learning was implemented for multi-label text classification. Positive samples were generated using the Regularized Dropout (R-Drop) method, while negative samples were reweighted. Supervised contrastive learning loss was incorporated during training to enhance the quality of neighbors retrieved by the KNN mechanism. Experimental results indicate that the proposed method achieves a Micro-F1 score of 92.17% on the electric power customer service work order dataset, surpassing the BERT model by 1.62 percentage points. Additionally, Micro-F1 scores of 75.2% and 88.5% were achieved on the public multi-label text classification datasets AAPD and RCV1-V2, respectively, demonstrating the practical feasibility of improving work order processing accuracy and validating the effectiveness of the proposed method in complex multi-label scenarios

Key words: multi-label text classification, power customer service tickets, contrastive learning, feature enhancement, pre-trained language model