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Research on cross-lingual multi-label patent classification based on pre-trained model

Author

Listed:
  • Yonghe Lu

    (Sun Yat-sen University
    Sun Yat-sen University)

  • Lehua Chen

    (Sun Yat-sen University)

  • Xinyu Tong

    (Sun Yat-sen University)

  • Yongxin Peng

    (Sun Yat-sen University)

  • Hou Zhu

    (Sun Yat-sen University
    University of Technology Sydney)

Abstract

Patent classification is an important part of the patent examination and management process. Using efficient and accurate automatic patent classification can significantly improve patent retrieval performance. Current monolingual patent classification models, on the other hand, are insufficient for cross-lingual patent tasks. Therefore, research into cross-lingual patent categorization is crucial. In this paper, we proposed a cross-lingual patent classification model based on the pre-trained model named XLM-R–CNN. Besides, we constructed a large patent dataset called XLPatent including Chinese, English, and German. We conducted experiments to evaluate model performance with several metrics. The experimental results showed that XLM-R–CNN achieved a classification accuracy of 73% and average precision of 94%.

Suggested Citation

  • Yonghe Lu & Lehua Chen & Xinyu Tong & Yongxin Peng & Hou Zhu, 2024. "Research on cross-lingual multi-label patent classification based on pre-trained model," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3067-3087, June.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:6:d:10.1007_s11192-024-05024-0
    DOI: 10.1007/s11192-024-05024-0
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    References listed on IDEAS

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    1. Shaobo Li & Jie Hu & Yuxin Cui & Jianjun Hu, 2018. "DeepPatent: patent classification with convolutional neural networks and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 721-744, November.
    2. Jie Hu & Shaobo Li & Jianjun Hu & Guanci Yang, 2018. "A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
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