用深度学习自动分类市民申诉，提速近一半还更准
AI Appeals Processor: A Deep Learning Approach to Automated Classification of Citizen Appeals in Government Services
- 用Word2Vec+LSTM模型处理文本，自动识别申诉类型
- 准确率达78%，处理时间比人工减少54%
- 适合政府机构想提升申诉处理效率的场景
全球各地政府部门面临市民申诉量持续增长，电子提交量近年显著上升。传统人工处理平均耗时20分钟，准确率仅67%，严重制约公共服务效率。本文提出AI Appeals Processor，一个基于微服务架构的系统，融合自然语言处理与深度学习技术，实现市民申诉的自动化分类与分派。我们在包含10,000条真实申诉的代表性数据集上评估多种方法——包括词袋+SVM、TF-IDF+SVM、fastText、Word2Vec+LSTM及BERT——覆盖三大类（投诉、申请、建议）和七个主题领域。实验表明，Word2Vec+LSTM架构在保持78%分类准确率的同时，将处理时间缩短54%，相比基于Transformer的模型在准确率与计算效率间取得更优平衡。
原文摘要 · Abstract (English)
Government agencies worldwide face growing volumes of citizen appeals, with electronic submissions increasing significantly over recent years. Traditional manual processing averages 20 minutes per appeal with only 67% classification accuracy, creating significant bottlenecks in public service delivery. This paper presents AI Appeals Processor, a microservice-based system that integrates natural language processing and deep learning techniques for automated classification and routing of citizen appeals. We evaluate multiple approaches -- including Bag-of-Words with SVM, TF-IDF with SVM, fastText, Word2Vec with LSTM, and BERT -- on a representative dataset of 10,000 real citizen appeals across three primary categories (complaints, applications, and proposals) and seven thematic domains. Our experiments demonstrate that a Word2Vec+LSTM architecture achieves 78% classification accuracy while reducing processing time by 54%, offering an optimal balance between accuracy and computational efficiency compared to transformer-based models.
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