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| 融入案件辅助句的低频和易混淆罪名预测 |
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郭军军1,2, 刘真丞1,2, 余正涛1,2, 黄于欣1,2, 相艳1,2
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1.昆明理工大学 信息工程与自动化学院, 云南 昆明 650500;2.云南省人工智能实验室(昆明理工大学), 云南 昆明 650500
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| 摘要: |
| 由于低频罪名数据量较少和易混淆罪名案情描述相似等原因，导致低频和易混淆罪名预测效果不佳.为了解决此类问题，通过构建案件辅助句，提出一种基于双向互注意力机制的案件辅助句融合方法，实现罪名预测.主要包括以下3部分：首先，基于司法领域知识构建案件辅助句，将案件辅助句作为案情描述和罪名之间的映射知识；然后，基于词级和字符级表征分别提取案情描述与案件辅助句多粒度特征；同时，借助案件辅助句与案情描述双向注意机制，获得具有辅助句倾向性的案情描述表征，并最终实现低频和易混淆罪名的预测.基于中国刑事案件公共数据集的实验结果表明：所提方法在F1值最大提升13.2%，准确率最大提升4.5%，低频罪名预测F1值提升4.3%，易混淆罪名预测F1值提升8.2%，所提算法显著地提升了低频和易混淆罪名的预测性能. |
| 关键词: 低频罪名 易混淆罪名 双向互注意力 多粒度编码 案件辅助句 |
| DOI：10.13328/j.cnki.jos.006028 |
| 分类号:TP18 |
| 基金项目:国家重点研发计划（2018YFC0830105，2018YFC0830101，2018YFC0830100）；国家自然科学基金（61972186，61762056，61472168，61866020）；云南省科技厅省级人培项目（KKSY201703015）；云南省基础研究专项面上项目（2019FB082，202001AT070047） |
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| Few Shot and Confusing Charges Prediction with the Auxiliary Sentences of Case |
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GUO Jun-Jun1,2, LIU Zhen-Cheng1,2, YU Zheng-Tao1,2, HUANG Yu-Xin1,2, XIANG Yan1,2
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1.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;2.Yunnan Key Laboratory of Artificial Intelligence(Kunming University of Science and Technology), Kunming 650500, China
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| Abstract: |
| Due to the insufficiency of few shot charges and the similarity of case descriptions for the confusing charges, the prediction performance of the existing methods for few shot charges and confusing charges is not promising. To address the forementioned drawbacks, a novel few shot and confusing charges prediction method is proposed, which is based on bi-direction mutual attention mechanism with the auxiliary sentences of case. For the proposed model, firstly, the auxiliary sentence of case via the judicial field is constructed, where the auxiliary sentence of case is considered as external knowledge for mapping the description of the case to the corresponding charge. Secondly, the multi-granularity characteristics of case description and the auxiliary sentence of case are extracted at the level of both word and character, respectively. At the same time, the auxiliary sentence of case and case description are used to build bi-direction mutual attention. Finally, the tendency representation of the case description with the guidance of the auxiliary sentence of case are derived, which improve the prediction accuracy of few shot and confusing charges. The experimental results conducted on the benchmark data of criminal cases show that the proposed model increases the F1 value and prediction accuracy by 13.2% and 4.5%, respectively, and increases the F1 values for the few shot charges and confusing charges by 4.3% and 8.2%, respectively, which significantly enhance the prediction performance for few shot and confusing charges. |
| Key words: few shot charge confusing charge bi-direction mutual attention multi-granular coding auxiliary sentence of case |
