Recent Advances in Natural Language Processing Techniques
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 194
Special Issue Editor
Special Issue Information
Dear Colleagues,
As Artificial Intelligence (AI) continues to integrate into every aspect of daily life and professional activities, the application of textual content across various media has underscored the crucial role of Natural Language Processing (NLP) in making sense of human language in all its forms. NLP stands at the intersection of data science, AI, and linguistics, offering essential tools and techniques for interpreting, understanding, and generating human language computationally.
Beginning with early attempts at machine translation, NLP has evolved significantly. Early methods typically relied on hand-crafted rules and templates, but with the rise of Machine Learning (ML), particularly in recent decades, the application of Deep Learning (DL) techniques has led to remarkable advancements. Today, NLP is used in various applications such as voice assistants, chatbots, sentiment analysis, summarization, and machine reading comprehension.
This Special Issue seeks to showcase the latest advancements and innovative approaches in NLP, encouraging contributions that span a broad range of NLP tasks and challenges. We aim to capture current trends in the field, addressing both foundational methods and cutting-edge techniques that propel NLP forward. We welcome submissions that explore any aspect of NLP, including but not limited to text mining, language generation, machine translation, multilingual systems, opinion mining, machine reading comprehension, information retrieval, chain of thought, and retrieval-augmented generation, among others. Submissions may range from experimental approaches and empirical research to review papers that provide insightful surveys of existing technologies and methodologies. We particularly encourage contributions that not only address technological aspects but also consider the societal impacts and ethical dimensions of NLP in domains like finance, healthcare, and education.
Dr. Jie Yang
Guest Editor
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Keywords
- large language models
- machine reading comprehension
- information retrieval
- text mining
- language generation
- machine translation
- multilingual systems
- chain of thought
- retrieval-augmented generation
- DL-based techniques and tools for NLP
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