Natural Language Processing Applications in Big Data

Dear Colleagues,

Recent developments in NLP, especially the application of large language models (LLMs), demonstrate the monumental shift in the ability of natural language processing (NLP) to analyse big data. However, there is still a significant gap between the theoretical advancements of NLP and their practical real-world applications. This Special Issue targets the practical application of natural language processing (NLP) in different disciplines and delves into how NLP enhances data analysis, decision making, and productivity across various sectors (such as finance, healthcare, and marketing) by automating and improving processes.

The aim of this Special Issue is to highlight the impact of NLP on data analysis across disciplines and address the critical challenges of big data, such as computational efficiency and cost, explainability, low-resource language applications, and sustainable development that meets the growing needs of industry.

Relevant topics for this Special Issue include, but are not limited to, the following areas:

• Computational social science and cultural analytics;
• Dialogue and interactive systems;
• Efficient/low-resource methods for NLP;
• Ethics, bias, and fairness;
• Finance NLP;
• Generation;
• Healthcare NLP;
• Information extraction;
• Information retrieval and text mining;
• Interpretability and analysis of models for NLP;
• Legal NLP;
• Linguistic theories, cognitive modelling, and psycholinguistics;
• Machine learning for NLP;
• Machine translation;
• Multilinguality and language diversity;
• Multimodality and language grounding to vision, robotics, and beyond;
• Question answering;
• Resources and evaluation;
• Sentiment analysis, stylistic analysis, and argument mining;
• Speech recognition, text-to-speech conversion, and spoken language understanding;
• Summarization.

Dr. Xingyi Song
Dr. Ye Jiang
Dr. Yunfei Long
Guest Editors

Manuscript Submission Information

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