Qwen TextCNN和BERT模型用于移动应用程序中的增强多标签新闻分类
Dawei Yuan1,2, Guojun Liang3, Bin Liu4
1School of Computer Science, Guangdong University of Science and Technology, Dongguan, 523083, China. yuandawei@gdust.edu.cn.
Scientific reports
|December 15, 2025
概括
这项研究比较了传统和大型语言模型 (LLM) 的移动新闻分类. 伯特模型在多标签任务中表现出色,而LSTM和MLP分类器在指令提示中显示出高精度.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习用于移动应用程序
背景情况:
- 移动新闻分类系统复杂且规模庞大.
- 对大型语言模型 (LLM) 进行传统模型的评估对于优化移动新闻分类至关重要.
研究的目的:
- 在中国移动应用中进行传统分类模型和LLM用于多标签新闻分类的比较研究.
- 评估BERT,Qwen (指令调和LoRA微调),TextCNN,LSTM和MLP分类器的性能.
主要方法:
- 对TextCNN,BERT和各种LLM (Qwen) 的比较分析.
- 使用指令调整和低级调整 (LoRA) 微调技术评估模型.
- 对平衡和不平衡数据集的分类器性能评估,重点关注多标签和二进制分类任务.
主要成果:
- 在平衡数据集上,BERT模型在多标签分类方面表现优异.
- 在二进制分类任务中,TextCNN更有效.
- LSTM和MLP分类器通过文本指令提示实现了高准确性,优于随机嵌入.
结论:
- 移动新闻分类的模型选择需要平衡技术能力与部署约束.
- 尽管存在阶级不平衡的挑战 (较低的宏观F1分数),相对绩效分析证实了这些发现.
- 该研究提供了关于在移动应用程序中优化汽车新闻分类的见解.
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