NLP ALGORITMLARI ASOSIDA YOZMA ISHLARNI AVTOMATIK BAHOLASH USULLARINING TAHLILI
DOI:
https://doi.org/10.5281/zenodo.20303401Keywords:
NLP, automatic assessment, written work, e-assessment, machine learning, deep learning, transformer models, semantic analysis, artificial intelligence, BERT, AES.Abstract
This article analyzes the theoretical and practical aspects of automatic assessment methods for written work based on Natural Language Processing (NLP) algorithms. The study studies the effectiveness of the main approaches used in automatic assessment systems, including statistical models, machine learning algorithms, and transformer models based on deep learning. It also examines the problems of semantic analysis, syntactic structure, and contextual relevance in assessing written work. The results of the study show that modern NLP algorithms provide high accuracy and objectivity in automating the assessment process, while also improving the quality of education.References
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