Machine Learning Approaches for Natural Language Processing
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 September 2025 | Viewed by 424
Special Issue Editors
Interests: large language model; backdoor attack; natural language processing; summary generation; code generation; radiology report summarization
Interests: machine learning; computer vision; large language model; multimodal learning; sentiment analysis
Special Issue Information
Dear Colleagues,
In recent years, the emergence of pre-trained large language models (LLMs) has brought transformative changes to the field of natural language processing (NLP). These advanced models have significantly expanded the scope of NLP applications, driving the development of artificial intelligence systems and enabling new possibilities for practical use cases. Recognizing the profound impact of these emerging technologies, it is crucial to explore their potential and understand their relationship with traditional approaches to shape the future of NLP.
The objective of this Special Issue is to present cutting-edge research in the field of NLP, with a particular focus on new theories, methodologies, and applications that drive the advancement of current technologies. We aim to delve into the utilization of state-of-the-art pre-trained language models, fostering a wide range of applications from text understanding to generation, and from single-modal to multi-modal learning. Furthermore, this Special Issue emphasizes the importance of interdisciplinary research, showcasing how NLP can be integrated with other domains such as computer vision, speech recognition, and healthcare to open new avenues for innovation.
Suggested themes for this Special Issue include, but are not limited to, the following:
- Novel NLP algorithms;
- Training and fine-tuning methodologies for LLMs;
- Interpretability of NLP models and LLMs;
- Domain-specific NLP applications;
- Multi-modal learning and alignment;
- NLP methods for low-resource languages;
- Ethics, bias, and fairness in NLP;
- Continual and online learning in NLP;
- Applications of NLP in education;
- NLP in intelligent healthcare systems.
Dr. Shuai Zhao
Dr. Mengchao Zhang
Dr. Deyu Lin
Guest Editors
Manuscript Submission Information
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Keywords
- large language models
- natural language processing
- machine learning
- multi-modal learning
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