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AI Appeals Processor: A Deep Learning Approach to Automated Classification of Citizen Appeals in Government Services

arXiv:2604.03672v1 Announce Type: new 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 optim

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Vladimir Beskorovainyi · · 1 min read · 6 views
#cs.CL #cs.AI

arXiv:2604.03672v1 Announce Type: new 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.

Executive Summary

This article presents AI Appeals Processor, a microservice-based system that utilizes natural language processing and deep learning techniques to automate the classification and routing of citizen appeals in government services. Evaluating multiple approaches on a dataset of 10,000 real citizen appeals, the authors demonstrate that a Word2Vec+LSTM architecture achieves 78% classification accuracy while reducing processing time by 54%. The results offer an optimal balance between accuracy and computational efficiency compared to transformer-based models. This innovative solution has significant implications for public service delivery, addressing bottlenecks in manual processing and improving citizen satisfaction. As government agencies worldwide face growing volumes of citizen appeals, AI Appeals Processor provides a promising solution to streamline their operations.

Key Points

  • ▸ AI Appeals Processor integrates natural language processing and deep learning techniques for automated classification and routing of citizen appeals.
  • ▸ The system achieves 78% classification accuracy with a 54% reduction in processing time using a Word2Vec+LSTM architecture.
  • ▸ The results demonstrate an optimal balance between accuracy and computational efficiency compared to transformer-based models.

Merits

Advancements in Automated Classification

The AI Appeals Processor showcases significant improvements in automated classification accuracy, achieving 78% with a 54% reduction in processing time. This advancement addresses the bottleneck in manual processing, enabling government agencies to efficiently manage growing volumes of citizen appeals.

Potential for Public Service Delivery

By streamlining the appeals processing system, AI Appeals Processor has the potential to enhance citizen satisfaction and improve public service delivery. This innovation is particularly relevant in the context of growing volumes of electronic submissions and limited government resources.

Demerits

Data Quality and Bias Concerns

The accuracy of the AI Appeals Processor relies heavily on the quality of the training data. Poor data quality or inherent biases in the dataset can negatively impact the system's performance, compromising the integrity of the appeals classification process.

Scalability and Maintenance Challenges

As government agencies expand the use of AI Appeals Processor, scalability and maintenance challenges may arise. Ensuring the system's performance and adaptability in diverse contexts will be crucial to its widespread adoption.

Expert Commentary

The AI Appeals Processor presents a promising solution for addressing the significant challenges faced by government agencies in processing citizen appeals. By leveraging deep learning techniques and natural language processing, the system achieves impressive accuracy and efficiency improvements. However, it is crucial to address the limitations and potential concerns associated with AI Appeals Processor, such as data quality and bias, scalability, and maintenance challenges. As government agencies consider adopting AI Appeals Processor, they must prioritize addressing these issues to ensure the system's effectiveness and integrity.

Recommendations

  • ✓ Government agencies should prioritize developing and implementing robust data quality assurance processes to ensure the integrity of the AI Appeals Processor.
  • ✓ Regular monitoring and evaluation of AI Appeals Processor's performance, including its accuracy and bias, are essential to address any emerging challenges and ensure the system's adaptability in diverse contexts.

Sources

Original: arXiv - cs.CL

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