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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 9

Guided words and collaborative sinkhorn in supervised topic model under multi-dimensional loss function

Xu Zhenshun1,2
Wang Zhenbiao1,2
Zheng Shunguo1,2
Su Mengyao1,2
Zhang Wenhao1,2
Tang Zengjin1,2
1. College of Compute Science & Engineering, North Minzu University, Yinchuan 750021, China
2. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2025.02.0035

Abstract

The topic model analyzes large volumes of data to uncover latent thematic structures and semantic relationships, categorizing and generalizing data according to themes, thereby enhancing information processing efficiency. To address issues faced by existing topic models, such as the collapse of semantic coherence between generated topics, the lack of supervised labels, and the absence of guidance from seed words, a novel supervised topic model incorporating a multidimensional loss function with Seed word-guided Sinkhorn optimization is proposed. This model designs a multidimensional loss function that combines adaptive reconstruction loss, supervision loss, and conditional variational autoencoder loss as cooperative strategies, while also introducing regularization and normalization methods to mitigate the issue of topic semantic collapse. Furthermore, the model integrates seed words with the Sinkhorn algorithm in the loss function, effectively resolving the issue of theme coverage, ensuring that the model remains focused on specific topics during the generation process and enhancing interpretability. Experimental results demonstrate that this model effectively addresses issues such as topic collapse and biased topic coverage, generating diverse and coherent topics and high-quality document-topic distributions, continually surpassing state-of-the-art baselines.

Foundation Support

宁夏自然科学基金资助项目(2021AAC03217)
宁夏重点研发计划(重点)项目(2023BDE02001)
银川市校企联合创新项目(2022XQZD009)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.02.0035
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 9

Publish History

[2025-05-21] Accepted Paper

Cite This Article

徐贞顺, 王振彪, 郑顺国, 等. 多维度损失函数下引导词协同sinkhorn的监督式主题模型 [J]. 计算机应用研究, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.02.0035. (Xu Zhenshun, Wang Zhenbiao, Zheng Shunguo, et al. Guided words and collaborative sinkhorn in supervised topic model under multi-dimensional loss function [J]. Application Research of Computers, 2025, 42 (9). (2025-05-27). https://doi.org/10.19734/j.issn.1001-3695.2025.02.0035. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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