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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 10

Confusing sample-driven inter-cluster optimization method for short text clustering

Enkaer Nuertai1,2,3
Ma Bo1,2,3
Wang Zhen1,2,3
Aizimaiti Ainiwaer1,2,3
Tuerhong Wusiman1,3
Yang Yating1,2,3
1. Xinjiang Technical Institute of Physics & Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Xinjiang Laboratory of Minority Speech & Language Information Processing, Urumqi 830011, China
DOI: 10.19734/j.issn.1001-3695.2025.03.0075

Abstract

Short text clustering aims to partition unlabeled short text instances into different semantic clusters. This task faces challenges from indistinguishable confusing samples and overlapping feature distributions between semantically similar clusters. To tackle these challenges, this paper proposes a Confusing Sample-Driven Inter-Cluster Distribution Optimization Method for Short Text Clustering. This method first sampled high-uncertainty instances based on information entropy as confusing samples, and selected their neighboring cluster samples as candidates. Then, it leveraged large language models to semantic discrimination and formed "confusing -positive -negative" triplets. Meanwhile, this method adopted a data augmentation method based on parameter random perturbation to generate instance-level positives for each sample. Finally, it performed joint optimization of inter-cluster distribution within a contrastive learning framework. Experimental results on four public short text datasets demonstrate that the proposed method outperforms existing state-of-the-art models, with an average accuracy improvement of 5.14% and an average normalized mutual information increase of 2.51%. Further analysis confirms that the method significantly enhanced semantic discrimination of confusing samples between clusters and effectively alleviated feature overlap between semantically similar clusters.

Foundation Support

天山英才培养计划项目(2023TSYCCX0041,2022TSYCCX0059)
新疆维吾尔自治区自然科学基金资助项目(2022D01D81,2022D01D04,2022D01B207)
新疆维吾尔自治区重点研发计划项目(2023B03024)
中国科学院青年创新促进会项目(Y2021112,Y2023118)
新疆维吾尔自治区上海合作组织科技伙伴计划及国际科技合作计划项目(2023E01019)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.03.0075
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 10

Publish History

[2025-06-04] Accepted Paper

Cite This Article

恩卡尔·奴尔太, 马博, 王震, 等. 易混淆样本驱动的簇间分布优化短文本聚类 [J]. 计算机应用研究, 2025, 42 (10). (2025-06-04). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0075. (Enkaer Nuertai, Ma Bo, Wang Zhen, et al. Confusing sample-driven inter-cluster optimization method for short text clustering [J]. Application Research of Computers, 2025, 42 (10). (2025-06-04). https://doi.org/10.19734/j.issn.1001-3695.2025.03.0075. )

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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