Neuro Cognitive and Knowledge Driven Natural Language Processing (NLP) in the Large Language Model (LLM) Era

Dear Colleagues,

Natural Language Processing (NLP) stands at the forefront of artificial intelligence, continuously evolving to tackle the ever-growing challenges posed by the surge of unstructured data from social media, digital articles, and other online sources. The advent of large language models (LLMs) has revolutionized this field, yet significant challenges remain, particularly in understanding the intricate nuances, contexts, and ambiguities of human language.

Despite advancements, interactive automation systems such as chatbots and Q/A system still struggle to fully emulate human understanding and contextual awareness. As the volume of unstructured data expands, there is an increasing need for NLP techniques that can more accurately capture and process the complexities of human communication. This includes the integration of neuro-cognitive approaches and knowledge-driven methodologies to enhance the capabilities of NLP systems.

This Special Issue aims to present a timely collection of original research contributions that advance the field of NLP in the era of large language models. We invite researchers and practitioners to submit their latest findings and innovative approaches related to the integration of neuro-cognitive and knowledge-driven techniques in NLP. The scope of this Special Issue includes, but is not limited to, the following topics:

• Information Extraction/Retrieval
• Affective Analysis
• Machine Translation
• Text Summarization
• Dialogue Systems
• Contextual Understanding
• Semantic Analysis
• Cognitive Modeling in NLP
• Knowledge-Enhanced NLP
• Human-Like Interaction in NLP Systems

We seek high-quality submissions that address the challenges and opportunities presented by these advanced NLP techniques. This Special Issue will serve as a valuable resource for those looking to stay at the cutting edge of NLP research and applications.

Dr. Yunfei Long
Guest Editor

Manuscript Submission Information

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