# Text classification dataset and analysis for Uzbek language

arXiv: https://arxiv.org/abs/2302.14494 · HTML: https://mlsanity.com/paper/2302.14494/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Establishing Baselines for Text Classification in Low-Resource Languages](https://mlsanity.com/paper/2005.02068/) (2020)
- [Cross-Lingual Word Embeddings for Turkic Languages](https://mlsanity.com/paper/2005.08340/) (2020)
- [SimRelUz: Similarity and Relatedness scores as a Semantic Evaluation dataset for Uzbek language](https://mlsanity.com/paper/2205.06072/) (2022)
- [A machine transliteration tool between Uzbek alphabets](https://mlsanity.com/paper/2205.09578/) (2022)
- [Uzbek Sentiment Analysis based on local Restaurant Reviews](https://mlsanity.com/paper/2205.15930/) (2022)
- [UzbekStemmer: Development of a Rule-Based Stemming Algorithm for Uzbek Language](https://mlsanity.com/paper/2210.16011/) (2022)
- [UzbekTagger: The rule-based POS tagger for Uzbek language](https://mlsanity.com/paper/2301.12711/) (2023)

## Built on by (most cited first, 3 of 3)

- [Recent Advancements and Challenges of Turkic Central Asian Language Processing](https://mlsanity.com/paper/2407.05006/) (2024)
- [UzMorphAnalyser: A Morphological Analysis Model for the Uzbek Language Using Inflectional Endings](https://mlsanity.com/paper/2405.14179/) (2024)
- [Beyond Subtokens: A Rich Character Embedding for Low-resource and Morphologically Complex Languages](https://mlsanity.com/paper/2602.21377/) (2026)

## Abstract (arXiv)

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.