Deep Learning for Natural Language Processing: A Review of Models and Applications

  • Authors

    • Renjbar Sh. Othman Duhok Polytechnic University, Technical Institute of Amedi, Department of Information Technology, Duhok, Kurdistan Region, Iraq and Akre University for Applied Sciences, Technical College of Informatics-Akre, Department of Information Technology, Akre, Kurdistan Region, ‎Iraq
    • Ibrahim Mahmood Ibrahim Akre University for Applied Sciences, Technical College of Informatics-Akre, Department of Computer Networks and Information Security, ‎Kurdistan Region, Iraq
    https://doi.org/10.14419/t1xnaq87

    Received date: April 24, 2025

    Accepted date: June 15, 2025

    Published date: August 29, 2025

  • Deep Learning; Natural Language Processing; Transformer Models; Neural Network Architectures; Sentiment Analysis; Text Classification; ‎Multimodal and Hybrid Models‎.

  • Abstract

    This review provides a critical analysis of the transformative impact of deep learning on the advancement of Natural Language Processing ‎‎(NLP). With the increasing volume of unstructured textual data, traditional rule-based and statistical methodologies have demonstrated limitations in effectively capturing the intricacies of human language. In contrast, deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer-based architectures such as BERT and GPT, have significantly ‎enhanced NLP capabilities by facilitating context-aware, scalable, and highly accurate language comprehension. The primary objective of this ‎review is to deliver a comprehensive synthesis of deep learning architectures utilized in essential NLP tasks, including sentiment analysis, ‎text classification, machine translation, and question answering. Additionally, it examines their evolution, key applications, and comparative ‎performance across various domains. By reviewing recent literature from 2021 to 2025, this analysis also emphasizes hybrid models, multimodal systems, and adaptations for low-resource environments. The goal is to identify emerging trends, challenges (e.g., interpretability, ‎computational cost), and future directions, including data augmentation, self-supervised learning, and cross-domain generalization, ultimately-‎ly guiding researchers towards the development of more adaptive and trustworthy NLP systems‎.

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  • How to Cite

    Othman , R. S. ., & Ibrahim , I. M. . (2025). Deep Learning for Natural Language Processing: A Review of Models and Applications. International Journal of Scientific World, 11(2), 71-78. https://doi.org/10.14419/t1xnaq87