Research on text evaluation based on natural language processing and GSDMM topic model

Bing Wang

doi:10.1117/12.3045223

30 September 2024 Research on text evaluation based on natural language processing and GSDMM topic model

Bing Wang

Proceedings Volume 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024); 132861K (2024) https://doi.org/10.1117/12.3045223
Event: Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 2024, Guangzhou, China

Abstract

Driven by natural language processing technology, topic models have shown a wide range of application value in fields such as text analysis, classification and clustering. The GSDMM topic model has been widely recognized in the field of topic modeling due to its ability to automatically infer the number of clusters, fast convergence, and efficient processing of sparse and high-dimensional short texts. This paper aims to explore the text evaluation research based on natural language processing and GSDMM (Gibbs Sampling and Distributed Memory Model) topic model, and carries out relevant practice in the text evaluation of project-based teaching course.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bing Wang "Research on text evaluation based on natural language processing and GSDMM topic model", Proc. SPIE 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 132861K (30 September 2024); https://doi.org/10.1117/12.3045223

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