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DEC-transformer: deep embedded clustering with transformer on Chinese long text

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Abstract

Long text clustering is of great significance and practical value in data mining, such as information retrieval, text integration, and data compression. Compared with short text clustering, long text clustering involves more semantic information representation and processing, making it a challenging problem. Most recent techniques merely rely on dynamic word embeddings from pre-training as a transfer learning or only based on a simple combination of transformers and traditional clustering methods, which still need to be expanded to short text due to the quadratic computational complexity. In this paper, a novel model combining transfer learning and dynamic feedback called deep embedded clustering with transformer(DEC-transformer) is proposed. To better capture the semantic relationships between sentences in documents, a novel transfer learning technology is firstly applied to long text clustering tasks for pre-training. Unlike previous papers, a two-stage training task is designed by treating semantic representation and text clustering as a united process, and the parameter is dynamically optimized by adaptive feedback to further improve efficiency. Experimental results on the test set show that the proposed model has made great progress in accuracy compared with several benchmarks. Furthermore, it also has good robustness and can get good performance on noisy datasets.

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Data availability statement

The datasets used in the experiment are open-source, and their links are as follows: Fudan Corpus: https://www.heywhale.com/mw/dataset/5d3a9c86cf76a600360edd04/content SogouCS Corpus: https://www.sogou.com/labs/resource/cs.php.

Notes

  1. The code of this work is available at https://github.com/Uchiha-Monroe/DEC-transformer.

  2. The data are available at https://www.kesci.com/home/dataset/5d3a9c86cf76a600360edd04.

  3. The data are available at https://hyper.ai/datasets/9270.

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Funding

This work was supported by The National Natural Science Foundation of China (No. 61806221).

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Authors and Affiliations

  1. Command and Control Engineering College, Army Engineering University of PLA, No.1 Haifu Xiang, Nanjing, 210007, Jiangsu, China

    Ao Zou, Wenning Hao, Gang Chen & Dawei Jin

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  1. Ao Zou
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  2. Wenning Hao
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  3. Gang Chen
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  4. Dawei Jin
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ao Zou, Gang Chen and Wenning Hao. The first draft of the manuscript was written by Ao Zou and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Wenning Hao.

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Zou, A., Hao, W., Chen, G. et al. DEC-transformer: deep embedded clustering with transformer on Chinese long text. Pattern Anal Applic 26, 1349–1362 (2023). https://doi.org/10.1007/s10044-023-01161-z

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