Text classification is an important task in Natural Language Processing
(NLP), where the goal is to categorize text data into predefined classes. In
this study, we analyse the dataset creation steps and evaluation techniques of
multi-label news categorisation task as part of text classification. We first
present a newly obtained dataset for Uzbek text classification, which was
collected from 10 different news and press websites and covers 15 categories of
news, press and law texts. We also present a comprehensive evaluation of
different models, ranging from traditional bag-of-words models to deep learning
architectures, on this newly created dataset. Our experiments show that the
Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) based
models outperform the rule-based models. The best performance is achieved by
the BERTbek model, which is a transformer-based BERT model trained on the Uzbek
corpus. Our findings provide a good baseline for further research in Uzbek text
classification.

本研究分析了多标签新闻分类任务的数据集创建步骤和评估技术，提供一个收集自十个不同新闻和出版网站的 15 类新闻、出版和法律文本的新文本分类优范，研究比较了从传统的词袋模型到深度学习架构的不同模型，实验结果显示 RNN 和 CNN 模型优于基于规则的模型，最佳性能 BERTbek 模型，为乌兹别克文本分类的进一步研究打下了基础。