Research on computational fluid dynamics literature mining methods using natural language processing

Qing Li; Yuting Duan; Tao Zheng; Li Li

doi:10.1117/12.3012277

21 December 2023 Research on computational fluid dynamics literature mining methods using natural language processing

Qing Li, Yuting Duan, Tao Zheng, Li Li

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701K (2023) https://doi.org/10.1117/12.3012277
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In our paper, we propose a methodology that uses natural language processing technology, including the Latent Dirichlet Allocation model and Word2Vec word embedding model, along with various machine learning and deep learning algorithms. This approach allows us to analyze Computational Fluid Dynamics literature accurately and efficiently. By applying these techniques to extract topic and word relationships from the literature data, we can discover hotspots, research directions, and emerging trends in the field. Our results validate the effectiveness of this approach, providing valuable insights. This research also offers a framework for researchers to analyze scientific literature, helping them understand development trends and explore knowledge associations in their respective fields.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qing Li, Yuting Duan, Tao Zheng, and Li Li "Research on computational fluid dynamics literature mining methods using natural language processing", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701K (21 December 2023); https://doi.org/10.1117/12.3012277

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