Advanced Natural Language Processing Technology and Applications

Dear Colleagues,

Natural language processing has rapidly evolved in recent years, driven by advancements in machine learning, deep learning, and artificial intelligence. This Special Issue aims to highlight the latest research and developments in the field, showcasing the innovative approaches, novel techniques, and practical applications shaping NLP's future.

Natural language processing focuses on enabling computers to understand, interpret, and generate human language. It encompasses various tasks, such as language understanding, sentiment analysis, machine translation, question answering, information retrieval, and text generation. With the increasing availability of large-scale language resources, the explosion of textual data, and the growing demand for intelligent language-based systems, NLP has become integral to many real-world applications and industries.

This year has witnessed a tremendous impact from large language models (LMM) in NLP. Models like GPT-3 and its successor have showcased remarkable capabilities in understanding and generating human-like text, paving the way for new possibilities and applications. The emergence of these models has sparked a surge of research and innovation in advanced NLP techniques.

One notable area where large language models have made significant strides is language understanding. These models have demonstrated remarkable performance in sentiment analysis, named entity recognition, and text classification tasks. Their ability to grasp the nuances of language and extract meaningful insights from vast amounts of textual data has opened up opportunities for more accurate and efficient language-based applications.

We invite the submission of high-quality, original contributions that address theoretical or practical issues related to the theme of the Special Issue.

Dr. Michał Marcińczuk
Guest Editor

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