Role of Artificial Intelligence in Natural Language Processing
This special issue belongs to the section "Computer Science & Engineering".
Special Issue Information
Dear Colleagues,
Focus: This Special Issue focuses on exploring the evolving role of artificial intelligence (AI) in advancing the field of natural language processing (NLP). It aims to provide a comprehensive examination of cutting-edge AI techniques, their applications in NLP, and the challenges and opportunities these technologies present.
Scope: Contributions are encouraged that address a variety of aspects, including (but not limited to) the following:
- Fundamental research on AI algorithms tailored for NLP tasks.
- Applications of AI in areas such as sentiment analysis, machine translation, speech recognition, and conversational agents.
- Ethical and security considerations, including biases in AI models, privacy concerns, societal implications, and mitigation of harmful outputs.
- Emerging topics, such as multimodal NLP, zero-shot learning, low-resource language processing, and explainable AI (XAI) in NLP.
- Application of AI-powered NLP systems across various domains including healthcare, education, security, law, and finance.
Purpose: This Special Issue aims to showcase research that advances state-of-the-art NLP capabilities through AI as well as investigating the practical implementation of AI in real-world NLP contexts.
Relationship to Existing Literature: This Special Issue will complement existing research by addressing the evolution of AI technologies and their specific application to NLP. By consolidating recent advancements and fostering discussions on unresolved challenges, the Special Issue seeks to serve as an impactful resource on this rapidly evolving topic. Specifically, this issue aims to offer a holistic perspective on how AI is reshaping the NLP landscape, and highlight interdisciplinary approaches that integrate AI-powered NLP in various areas including, but not limited to, linguistics, cognitive science, and computer science.
Prof. Dr. John Hastings
Dr. Sherri Weitl-Harms
Dr. Khandaker Mamun Ahmed
Guest Editors
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Keywords
- AI methodologies in NLP: deep learning, large language models (LLMs), transformers, self-attention mechanisms, transfer learning, and reinforcement learning (RL)
- applications of AI in NLP, multimodal and multilingual NLP, and low-resource languages
- emerging topics in NLP (e.g., autonomous text generation, XAI, ethical and secure AI)
- domain-specific applications (e.g., healthcare and medicine, education, finance, social media analytics, and cybersecurity)
- security-related topics in NLP (e.g., adversarial attacks and defenses in NLP, privacy-preserving NLP, detection of malicious text, ethical hacking, and insider threats)
- challenges and open problems (e.g., bias and fairness, data scarcity, computational efficiency, generalization, and robustness)
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Published Papers
Electronics - ISSN 2079-9292