Abstract
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.
Abstract (translated)
全球政府机构正面临日益增长的公民上诉量，其中电子提交方式在近年显著增加。传统人工处理每起上诉平均耗时20分钟，分类准确率仅67%，造成公共服务交付的严重瓶颈。本文提出AI上诉处理器——一个基于微服务的系统，集成自然语言处理与深度学习技术，实现公民上诉的自动分类与路由。我们在涵盖三类主要诉求（投诉、申请、建议）及七个主题领域的10,000条真实公民上诉数据集上，评估了多种方法：包括词袋模型结合支持向量机、TF-IDF结合支持向量机、fastText、Word2Vec结合长短期记忆网络，以及BERT。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%，相较于基于Transformer的模型，在准确率与计算效率间实现了最佳平衡。
URL
https://arxiv.org/abs/2604.03672
