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:2019,28(3):191-195
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基于注意力机制的BiGRU判决结果倾向性分析
(1.中国科学院 沈阳计算技术研究所, 沈阳 110168;2.中国科学院大学 北京 100049;3.辽宁省人民检察院沈阳铁路运输分院, 沈阳 110001;4.沈阳东软系统集成工程有限公司, 沈阳 110000)
Attention-Based BiGRU on Tendency Analysis of Judgment Results
(1.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Shenyang Railway Transportation Branch of Liaoning People's Procuratorate, Shenyang 110001, China;4.Shenyang Neusoft System Integration Engineering Co. Ltd., Shenyang 110000, China)
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投稿时间:2018-09-07    修订日期:2018-10-08
中文摘要: 对裁判文书中判决结果的倾向性分析是完成律师推荐系统的前提,如何高效的实现判决结果倾向性分析是本文的重点.本文提出了基于注意力机制和BiGRU的判决结果倾向性分析模型.首先,训练词向量,得到词向量表;然后,通过查找词向量表,将文书数据转化为词向量序列,将词向量序列作为输入来训练判决结果倾向性分析模型.实验结果表明:注意力机制和BiGRU算法在判决结果倾向性分析中具有一定的有效性.该模型能够对裁判文书中判决结果的倾向性做一个合理的判断,为后期律师推荐系统的实现提供一个合理的评分依据.
Abstract:The tendency analysis of the judgment results in the judgment documents is the premise of completing the lawyer recommendation system. How to effectively realize the tendency analysis of judgment results has been focused on. This study proposes a biased analysis model based on attention mechanism and BiGRU. First, training the word embedding to obtain a word embedding table. Then, by looking up the word embedding table, the document data is transformed into a word embedding sequence, and the word embedding sequence is used as input to train the judgment result orientation analysis model. The experimental results show that the attention mechanism and BiGRU algorithm have certain effectiveness in the analysis of decision results. The model can make a reasonable judgment on the propensity of the judgment results in the judgment documents, and provide a reasonable scoring basis for the realization of the later lawyer recommendation system.
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基金项目:水专项课题(2018ZX07601001);环境质量预警项目(2017CXCY-C-06)
引用文本:
王宁,李世林,刘堂亮,赵伟.基于注意力机制的BiGRU判决结果倾向性分析.计算机系统应用,2019,28(3):191-195
WANG Ning,LI Shi-Lin,LIU Tang-Liang,ZHAO Wei.Attention-Based BiGRU on Tendency Analysis of Judgment Results.COMPUTER SYSTEMS APPLICATIONS,2019,28(3):191-195

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