WPCM: A Multi-Label Patent Classification Method Based on Weakly Supervised Learning

Dechao Wang, Yongjie Li, Jian Zhu, Xiaoli Tang

2025

Abstract

Current research on automatic patent classification predominantly focuses on reclassification within existing patent classification systems. This study aims to enhance the classification performance of automatic patent classification tasks in scenarios lacking annotated data, broaden the application scope of patent classification, and establish a foundation for mapping patents to real-world scenarios or subject-specific classification systems. To achieve this, we propose a weakly supervised multi-label patent classification method. This approach captures semantic similarity features both within patent documents and between patents and hierarchical classification labels through a two-stage process involving contrastive learning and comprehensive classification, enabling the automatic classification of unlabeled patents. Experimental results on a medical patent dataset demonstrate the efficacy of the proposed method. The model achieves Precision scores of 0.8237, 0.5743, and 0.4467 at the subclass, main group, and subgroup levels, respectively. Comparative and ablation experiments further validate the effectiveness of each component module within the method.

Paper Citation

in Harvard Style

Wang D., Li Y., Zhu J. and Tang X. (2025). WPCM: A Multi-Label Patent Classification Method Based on Weakly Supervised Learning. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN , SciTePress, pages 286-293. DOI: 10.5220/0013708500004000

in Bibtex Style

@conference{kdir25,
author={Dechao Wang and Yongjie Li and Jian Zhu and Xiaoli Tang},
title={WPCM: A Multi-Label Patent Classification Method Based on Weakly Supervised Learning},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2025},
pages={286-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013708500004000},
isbn={},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - WPCM: A Multi-Label Patent Classification Method Based on Weakly Supervised Learning
SN -
AU - Wang D.
AU - Li Y.
AU - Zhu J.
AU - Tang X.
PY - 2025
SP - 286
EP - 293
DO - 10.5220/0013708500004000
PB - SciTePress