Cutting-Edge Neural Networks for NLP (Natural Language Processing)
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 47
Special Issue Editor
Interests: big data; natural language processing; knowledge graph; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue on "Cutting-Edge Neural Networks for NLP (Natural Language Processing)" aims to explore the latest advancements in neural network architectures and their transformative impact on the field of NLP. The scope includes, but is not limited to, the following topics:
1) Advanced Architectures: Examination of state-of-the-art neural network models such as transformers, BERT, GPT, and their variations. Proposing new architectures in areas such as self-supervised or environment-aware continuous learning; machine consciousness learning, including the emotion and sentiment representations; and first-principle learning. This includes innovations in model design, training techniques, and performance enhancements.
2) Application and Performance: Analysis of how these cutting-edge models improve performance across various NLP tasks like machine translation, sentiment analysis, text summarization, and personalized question answering. Studies highlighting benchmarks and real-world applications are encouraged.
3) Multimodal and Multilingual Approaches: Exploration of models that integrate text with other data types (e.g., images, audio, and time series) and those that handle multiple languages, emphasizing improvements in comprehension and generation across diverse languages and contexts.
4) Ethical and Practical Considerations: Discussions on the ethical implications, biases, and societal impact of deploying advanced neural networks in NLP. This includes addressing challenges related to fairness, truth discovery, accountability, and transparency.
Future Directions and Emerging Trends: Insights into upcoming trends in NLP, potential breakthroughs, and the future trajectory of neural networks in processing natural language.
This Special Issue seeks contributions from researchers, practitioners, and industry experts to present novel research findings, case studies, and reviews that push the boundaries of current NLP capabilities.
Prof. Dr. Xue Li
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- advanced architectures
- application and performance
- multimodal and multilingual approaches
- ethical and practical considerations
