基于BERT-DPCNN的警情文本分类研究  

Police Report Classification Based on BERT-DPCNN

作　　者：张静[1] 高子信 丁伟杰[1] Zhang Jing;Gao Zixin;Ding Weijie(Department of Computer and Information Security,Zhejiang Police College,Hangzhou 310051,China)

机构地区：[1]浙江警察学院计算机与信息安全系,杭州310051

出　　处：《数据分析与知识发现》2025年第2期48-58,共11页Data Analysis and Knowledge Discovery

基　　金：浙江省公益技术研究计划项目(项目编号:LGF21G030001);教育部人文社会科学研究规划课题(项目编号:22YJA840004)的研究成果之一。

摘　　要：【目的】对海量警情文本数据进行智能化分类。【方法】针对警情文本分类任务,提出一个基于BERTDPCNN的文本分类模型。采用BERT预训练模型生成文本词向量,通过优化DPCNN模型中的激活函数和改进动态学习率提高分类性能。【结果】将BERT-DPCNN与BERT、BERT-CNN、BERT-RCNN、BERT-RNN、BERT-LSTM、ERNIE等6类模型进行对比实验,结果表明BERT-DPCNN的准确率、召回率和精准率最佳。在二分类任务中,BERT-DPCNN的准确率达到98%以上,十一分类任务中,其准确率达到82%以上。【局限】模型参数较多,实验次数有限,有待进一步测试。【结论】基于BERT-DPCNN的文本分类模型能有效提升警情文本分类的准确率,为公安部门进行警情分析和研判提供数据支撑。[Objective]This paper proposes a new model to effectively classify massive police reports.[Methods]We constructed a text classification model based on BERT-DPCNN.Then,we used the BERT pre-trained model to generate word vectors.The model improved the classification performance by optimizing the activation function in the DPCNN model and enhancing the dynamic learning rate.[Results]We conducted comparative experiments between BERT-DPCNN and six other models,including BERT,BERT-CNN,BERT-RCNN,BERT-RNN,BERTLSTM,and ERNIE.The BERT-DPCNN achieved the best accuracy,recall,and precision.In the binary classification tasks,the accuracy of BERT-DPCNN exceeded 98%.In the eleven-category tasks,the model's accuracy exceeded 82%.[Limitations]The model has many parameters,and the limited number of experiments calls for further testing.[Conclusions]The new model effectively improves the accuracy of police report classification,providing data support for police departments in analyzing and assessing police incidents.

关 键 词：BERT DPCNN 警情文本分类 激活函数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]