Conference Paper2025

TurkRetNet: A Novel Retentive Network for Turkish Text Classification

Jahanggir Hossain Setu, Syed Tangim Pasha, Ashraful Islam, Nabarun Halder, Asif Mahmud, M Ashraful Amin

2025 IEEE 23rd Student Conference on Research and Development (SCOReD)

IEEE, pp. 1-6

DOI: 10.1109/scored68498.2025.11399052URL

Abstract

In the era of rapidly growing digital information, the classification of unstructured data from sources, e.g., Internet news articles, has become increasingly challenging. This study introduces the Turkish Retentive Network (TurkRetNet), a novel neural network architecture implemented to improve text classification tasks specifically for the Turkish language, a low-resource language with limited Natural Language Processing (NLP) resources. To address the common issue of class imbalance in real-world datasets, this study also implemented the KeNet data augmentation technique. The performance results showed that TurkRetNet considerably outperformed traditional models, e.g., Bidirectional Encoder Representations from Transformer for Turkish (BERTurk), and Convolutional BERT for Turkish (ConvBERTurk). The balanced dataset resulted in substantial improvements over the imbalanced dataset, with classification accuracy increasing by 6.06%, macro precision by 7.89%, macro recall by 9.52%, and macro F1-score by 8.86%. This research emphasises the efficacy of combining advanced neural architectures with effective data augmentation approaches that specifically target the distinct issues presented by low-resource languages. The combined application of TurkRetNet and KeNet set a new benchmark in Turkish text classification tasks. This study contributes to the advancement of NLP for under-resourced languages.

Keywords

  • class imbalance
  • low-resource languages
  • news classification
  • Turkish language processing

CCDS Authors

Syed Tangim Pasha
Syed Tangim Pasha

Syed Tangim Pasha

  • Collaborator, HCI Wing

  • Lecturer

    Department of Computing and Information System

    Daffodil International University, Bangladesh

Published at

UbiComp

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, Pattern Recognition, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Nabarun Halder
Nabarun Halder

Nabarun Halder

  • Research Assistant, CCDS

Published at

UbiComp, IJCNN, HCII, Pattern Recognition, IEEE Access

Asif Mahmud
Asif Mahmud

Asif Mahmud

  • Co-Director, IPT Wing

  • Lecturer

    Department of Computer Science and Engineering

    Independent University, Bangladesh (IUB)

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, Pattern Recognition, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

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