Hybrid artificial intelligence architectures for automatic text correction in the Kazakh language
Laura Baitenova, Saule Tussupova, Saken Mambetov, Gauhar Munaitbas, Gulnar Mukhamejanova · 2025
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Description
Laura Baitenova and co-authors build a hybrid system that corrects text in Kazakh, a language whose words stack many endings. Their 2025 paper notes the trade-offs of older tools: rule-based analyzers cover everything but stay rigid, while neural models handle ambiguity yet hunger for data. The team merges finite-state rules, a random-field model, and two transformer networks.
They also built a fresh corpus of 150,000 annotated sentences from varied sources for training and testing. The Kazakh-tuned transformer beat the multilingual one on accuracy, F1 score, and speed. The hybrid design pairs full rule coverage with neural judgment on hard cases like borrowed words. Uses include spell checking, search, and translation, with lessons for other Turkic languages.
Catalog record
| CREATOR | Laura Baitenova, Saule Tussupova, Saken Mambetov, Gauhar Munaitbas, Gulnar Mukhamejanova |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 82 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |