山东大学学报 (工学版) ›› 2026, Vol. 56 ›› Issue (4): 10-16.doi: 10.6040/j.issn.1672-3961.0.2025.137
• 机器学习与数据挖掘 • 上一篇
刘彦北1,2,赵敏全3*,谭松泰3,周李良3, 董新然2,4
Liu Yanbei1,2, Zhao Minquan3*, Tan Songtai3, Zhou Liliang3, Dong Xinran2,4
摘要: 合同风险检测有助于预防项目风险和保障权益,提高合同审查效率。然而,现有方法在合同文本的复杂语义理解和多尺度特征提取方面仍存在不足,难以兼顾全局语义关联与局部关键信息的捕捉。本研究提出一种基于多通道双向编码器表示模型(bidirectional encoder representations from transformers, BERT)的预训练方法MC-BERT(multi-channel BERT),用于合同文本检测。利用BERT预训练模型提取文本的全局特征,并设计多通道卷积模块提取文本的多尺度局部特征,联合注意力机制自适应学习全局与局部特征的权重分布,以精确提取文本的关键信息。在公开的合同文本数据集上,MC-BERT的合同风险判别准确率比现有的流行方法提升4.6%,验证所提模型的有效性。所提出模型融合BERT预训练、多通道卷积模块以及注意力机制的优点,为文本检测领域提供了新的研究思路和技术支持。
中图分类号:
| [1] 朱璋颖, 陆亦恬, 唐祝寿, 等. 基于隐私政策条款和机器学习的应用分类[J]. 通信技术, 2020, 53(11): 2749-2757. Zhu Zhangying, Lu Yitian, Tang Zhushou, et al. Application classification based on privacy policy terms and machine learning[J]. Communications Technology, 2020, 53(11): 2749-2757. [2] 高恒源. 基于深度学习的合同风险检测方法及应用[D]. 保定: 河北大学, 2024: 1-3. Gao Hengyuan. Contract risk detection method and application based on deep learning[D]. Baoding: Hebei University, 2024: 1-3. [3] 曾寒毓. 合同风险预判技术研究及应用[D]. 绵阳: 西南科技大学, 2021: 2-5. Zeng Hanyu. Research and application of contract risk prediction technology[D]. Mianyang: Southwest University of Science and Technology, 2021: 2-5. [4] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[PP/OL]. V2.(2013-09-07)[2025-03-05]. https://arxiv.org/abs/1301 [5] Guntamukkala N, Dara R, Grewal G. A machine-learning based approach for measuring the completeness of online privacy policies[C] //2015 IEEE 14th International Conference on Machine Learning and Applications. 2015, Miami, USA: IEEE, 2016: 289-294. [6] Lippi M, Palka P, Contissa G, et al. CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service[J]. Artificial Intelligence and Law, 2019, 27: 117-139. [7] Hendrycks D, Burns C, Chen A, et al. CUAD: an expert-annotated NLP dataset for legal contract review[PP/OL] V2.(2021-11-08)[2025-03-05].https://doi.org/10.48550/arXiv.2103.06268 [8] Torre D, Abualhaija S, Sabetzadeh M, et al. An AI-assisted approach for checking the completeness of privacy policies against GDPR[C] //2020 IEEE 28th International Requirements Engineering Conference. 2020, Zurich, Switzerland: IEEE, 2020: 136-146. [9] 周红, 王书钰, 黄文路. 基于NLP技术的建设工程合同风险智能检测框架研究[J]. 建筑经济, 2021, 42(6): 94-98. Zhou Hong, Wang Shuyu, Huang Wenlu. Research on intelligent detection framework of construction contract risks based on NLP[J]. Construction Economy, 2021, 42(6): 94-98. [10] 王书钰. 基于NLP和深度学习的建设工程合同条款缺漏风险智能化检测研究[D]. 厦门: 厦门大学, 2021: 11-13. Wang Shuyu. Research on intelligent detection of missing risk of construction project contract clauses based on NLP and deep learning[D]. Xiamen:Xiamen University, 2021: 11-13. [11] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C] //NeurIPS 2017. Long Beach, USA:Curran Associates, Inc, 2017: 5998-6008. [12] 王金政, 杨颖, 余本功. 基于多头协同注意力机制的客户投诉文本分类模型[J]. 数据分析与知识发现, 2023, 7(1): 128-137. Wang Jinzheng, Yang Ying, Yu Bengong. Classifying customer complaints based on multi-head co-attention mechanism[J]. Data Analysis and Knowledge Discovery, 2023, 7(1): 128-137. [13] 李浩君, 王耀东, 汪旭辉. 中文短文本情感分类: 融入位置感知强化的Transformer-TextCNN模型研究[J]. 计算机工程与应用, 2025, 61(11): 216-226. Li Haojun, Wang Yaodong, Wang Xuhui. Chinese short text sentiment classification: research on Transformer-TextCNN model with location-aware enhancement[J]. Computer Engineering and Applications, 2025, 61(11): 216-226. [14] Kim Y. Convolutional neural networks for sentence classification[PP/OL]. V2.(2014-09-03)[2025-03-05].https://doi.org/10.48550/arXiv.1408.5882 [15] 国家市场监督管理总局. 全国合同示范文本库[EB/OL].(2022-06-17)[2025-03-05]. https://htsfwb.samr.gov.cn/ [16] Koroteev, Mikhail V. BERT: a review of applications in natural language processing and understanding[PP/OL].(2021-03-22)[2025-03-05]. https://arxiv.org/abs/2103.11943 [17] Cui Y, Che W, Liu T, et al. Revisiting pre-trained models for Chinese natural language processing[PP/OL]. V2.(2020-11-02)[2025-03-05]. https://doi.org/10.48550/arXiv.2004.13922 [18] Yan H, Yi B S, Li H X, et al. Sentiment knowledge-induced neural network for aspect-level sentiment analysis[J]. Neural Computing and Applications, 2022, 34(24): 22275-22286. [19] Hong Y Z, Yu X G, He N, et al. FASPell: a fast, adaptable, simple, powerful Chinese spell checker based on DAE-decoder paradigm[C] //Proceedings of the 5th Workshop on Noisy User-generated Text(W-NUT 2019). Hong Kong, China:Association for Computational Linguistics, 2019: 160-169. [20] Cheng X Y, Xu W D, Chen K L, et al. SpellGCN: incorporating phonological and visual similarities into language models for Chinese spelling check[PP/OL]. V2.(2020-03-13)[2025-03-05]. https://arxiv.org/abs/2004.14166 [21] Zhang Riqing, Pang Chao, Zhang Chuanqiang, et al. Correcting Chinese spelling errors with phonetic pre-training[C] //Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. [S.l.] : Association for Computational Linguistics, 2021: 2250- 2261. [22] Xu Hengda, Li Zhongli, Zhou Qingyu, et al. Read, listen, and see: leveraging multimodal information helps Chinese spell checking[C] //Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. [S.l.] : Association for Computational Linguistics, 2021: 716-728. [23] Sun Y, Wang S H, Li Y K, et al. ERNIE 2.0: a continual pre-training framework for language understanding[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(5): 8968-8975. [24] Loshchilov I, Hutter F. Decoupled weight decay regularization[PP/OL]. V3.(2019-01-04)[2025-03-05]. https://arxiv.org/abs/1711.05101 [25] Van der maaten L. Accelerating t-SNE using tree-based algorithms[J]. J Mach Learn Res, 2014, 15: 3221-3245. |
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