AI Appeals Processor: A Deep Learning Approach to Automated Classification of Citizen Appeals in Government Services
Government agencies must register, classify and route every citizen appeal within statutory time limits, and much of this work is still done by hand. We describe AI Appeals Processor, a classification and routing component deployed in a CPU-only government environment, and report what its evaluation and deployment taught us. On 10,000 real Russian-language appeals from a cross-domain dataset, we compare Bag-of-Words and TF-IDF with SVM, fastText, Word2Vec+LSTM and multilingual BERT on a three-way appeal-type task. On a held-out test set of 1,500 appeals, BERT reaches 82% accuracy and Word2Vec+LSTM 78%, against 67% for individual operators measured on an expert-adjudicated gold standard. W...
| Authors | Vladimir Beskorovainyi |
|---|---|
| Primary category | cs.CL |
| Published | 4 April 2026 |
| Last updated at source | 8 October 2026 |
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