A SINGLE-LAYER FEEDFORWARD NEURAL NETWORK MODEL FOR MORPHOLOGICAL STEMMING OF UZBEK WORDS

U. I. Salaev

Samarqand davlat universiteti ilmiy tadqiqotlar axborotnomasi · 2026-yil

Annotatsiya

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.

Maqola ma’lumotlari
MualliflarU. I. Salaev
JurnalSamarqand davlat universiteti ilmiy tadqiqotlar axborotnomasi
Nashr sanasi2026-07-01
Jild2
Son3
Betlar135-141
DOI10.59251/2181-3973.2025.v1.138.1.4018

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