Deep Learning Approaches for Natural Language Processing

Dear Colleagues,

Rapid advancements in deep learning have revolutionized the field of natural language processing (NLP), enabling unprecedented capabilities in understanding, generating, and interacting with human language. This Special Issue of Electronics will focus on exploring the latest developments, challenges, and applications in deep learning in NLP. It will bring together researchers, practitioners, and industry experts to share cutting-edge methodologies, innovative models, and transformative insights.

Deep learning has led to the introduction of powerful architectures, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), transformers, and large-scale pre-trained language models, such as GPT, BERT, and T5, that have significantly advanced core NLP tasks. These include machine translation, text summarization, question answering, sentiment analysis, and named entity recognition. While these techniques have reshaped the boundaries of NLP performance, they also present challenges related to computational demands, data scarcity, interpretability, and fairness.

This issue invites contributions that address these challenges and expand the horizons of deep learning in NLP. Topics of interest include, but are not limited to, the following:

• Novel neural architectures and optimization techniques for NLP;
• Advances in pre-training, fine-tuning, and transfer learning for linguistic tasks;
• Resource-efficient deep learning methods for NLP on edge devices;
• Multilingual and cross-lingual models for diverse language applications;
• Ethical concerns, including bias mitigation, fairness, and transparency in NLP systems;
• Case studies highlighting real-world applications in industries such as healthcare, education, and finance.

Additionally, this issue encourages submissions that bridge deep learning and linguistics, offering insights into how neural models align with, or diverge from, human language processing. Explorations of hybrid systems that integrate symbolic reasoning and deep learning for more robust language understanding are also welcome.

By providing a platform for groundbreaking research and practical advancements, this Special Issue will foster innovation and collaboration, driving the next generation of NLP systems. Researchers and practitioners are invited to submit original research articles, comprehensive reviews, and insightful case studies to contribute to this vibrant area of study.

Prof. Dr. Aleksandra Świetlicka
Prof. Dr. Aleksandra Kawala-Sterniuk
Dr. Dariusz Mikołajewski
Guest Editors

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• natural language processing
• large language models
• machine learning
• speech analysis