Acta Scientiarum Naturalium Universitatis Pekinensis ›› 2024, Vol. 60 ›› Issue (1): 79-88.DOI: 10.13209/j.0479-8023.2023.077

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Bi-Attention Text-Keyword Matching for Law Recommendation

DING Na1, LIU Peng2, †, SHAO Huipeng3, WANG Xuekui4   

  1. 1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116 2. National Joint Engineering Laboratory of Internet Applied Technology of Mines, Xuzhou 221008 3. Legal Team of Tongshan Branch of Xuzhou Public Security Bureau, Xuzhou 221100 4. Alibaba Group, Hangzhou 311121
  • Received:2023-05-18 Revised:2023-07-31 Online:2024-01-20 Published:2024-01-20
  • Contact: LIU Peng, E-mail: liupeng(at)


丁娜1, 刘鹏2,†, 邵惠鹏3, 王学奎4   

  1. 1. 中国矿业大学信息与控制工程学院, 徐州 221116 2. 矿山互联网应用技术国家地方联合工程实验室, 徐州 221008 3. 江苏省徐州市铜山区公安局法制大队, 徐州 221100 4. 阿里巴巴集团有限公司, 杭州 311121
  • 通讯作者: 刘鹏, E-mail: liupeng(at)
  • 基金资助:


This paper proposed a bi-directional attention based text-keyword matching model for law recommendation (BiAKLaw). In this model, BERT is utilized as a basic matching model, bi-directional attention mechanism is implemented to extract token-level alignment features and keyword-level differential features, and these features are fused with keyword attentive semantic representations for a better matching effect. The experimental results on the traffic accident and intentional injury datasets demonstrate that, compared with BERT, the proposed model increases F1 evaluation metric by 3.74% and 3.43% respectively.

Key words: law recommendation, case fact, text matching, attention mechanism


提出一种双向注意力文本关键词匹配的法条推荐模型(BiAKLaw)。该模型以预训练语言模型 BERT 作为基础匹配模型, 利用双向注意力机制提取字符级对齐特征和关键词差异特征, 融合对齐特征、差异特征和关键词语义表征来提升匹配效果。在裁判文书交通肇事和故意伤害数据集上的实验结果表明, 与BERT模型相比, BiAKLaw在评价指标F1上分别提升3.74%和3.43%。

关键词: 法条推荐, 案件事实, 文本匹配, 注意力机制