This paper presents a single-layer feedforward neural network for morphological stemming of Uzbek words. Each word is encoded using character-level one-hot vectors (810-dimensional input: 27 characters × 30 positions). The model uses sigmoid activation, MSE loss, and gradient descent backpropagation. A systematic hyperparameter search over epochs E ∈ {20,30,40}, learning rates η ∈ {0.5,0.6}, and thresholds θ ∈ {0.70–0.85} across 24 configurations on 3,441 word–stem pairs yields a peak accuracy of 98.34% (E=40, η=0.6, θ=0.70). The results demonstrate that minimal neural architectures can effectively model agglutinative morphology in under-resourced Turkic languages.
| Mualliflar | U. I. Salaev |
|---|---|
| Jurnal | Samarqand davlat universiteti ilmiy tadqiqotlar axborotnomasi |
| Nashr sanasi | 2026-07-01 |
| Jild | 2 |
| Son | 3 |
| Betlar | 135-141 |
| DOI | 10.59251/2181-3973.2025.v1.138.1.4018 |
DOI: 10.59251/2181-3973.2025.v1.138.1.4018 · Maqolaning asl sahifasi
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