案件罪名认定是司法业务的重要环节，尚缺乏有效的智能辅助工具和手段.针对案件定罪的难点问题，提出一种结合张量分解和双向LSTM （Long Short-Term Memory）神经网络的案件定罪方法.该方法将案件数据表示为张量，并在张量分解过程中引入模板张量.模板张量可以在双向LSTM神经网络分类模型的训练过程不断的被优化，使得分解后的核心张量包含更加有效的张量结构和特征信息，有助于提高后续分类模型的准确性，实现案件罪名的精准认定.实验结果表明：所提出的基于张量分解和双向LSTM的司法案件定罪方法比现有方法具有更好的准确性.
Abstract
Conviction in judicial cases is an important part of judicial business, but there is still a lack of effective intelligent auxiliary tools and methods. Aiming at the difficult problem of conviction in judicial cases, a method combining tensor decomposition and Bi-LSTM neural network is proposed. This method represents the case data as a tensor and introduces a template tensor in the tensor decomposition process. The template tensor can be continuously optimized during the training process of Bi-LSTM neural network classification model, so that the decomposed core tensor contains more effective tensor structure and feature information, which is helpful to improve the accuracy of the subsequent classification model and realize the accurate conviction in judicial cases. The experimental results show that the proposed method for conviction in judicial cases based on tensor decomposition and Bi-LSTM has better accuracy than the existing methods.
关键词
张量分解 /
双向长短期记忆 /
模板张量 /
案件定罪 /
文本分类
{{custom_keyword}} /
Key words
tensor decomposition /
Bi-LSTM /
template tensor /
conviction in judicial cases /
text classification
{{custom_keyword}} /
中图分类号：
TP183
{{custom_clc.code}}
({{custom_clc.text}})
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 黄俏娟,罗旭东.人工智能与法律结合的现状及发展趋势[J].计算机科学,2018,45(12):1-11. Huang Qiao-juan,Luo Xu-dong.State-of-the-art and development trend of artificial intelligence combined with law[J].Computer Science,2018,45(12):1-11.(in Chinese)
[2] Octavia-Maria S,Marcos Z,Shervin M,Mihaela V,Liviu P D,Josef V G.Exploring the use of text classification in the legal domain[A].Proceedings of the Second Workshop on Automated Semantic Analysis of Information in Legal Texts[C].London,UK:ACM,2017.22-27.
[3] Octavia-Maria S,Marcos Z,Mihaela V,Josef V G.Predicting the law area and decisions of French supreme court cases[A].Proceedings of the International Conference Recent Advances in Natural Language Processing[C].Varna,Bulgaria:ACM,2017.56-63.
[4] 林志宏,池宏,许保光.基于卷积神经网络的公安案件文本语义特征提取方法研究[J].数学的实践与认识,2017,46(17):129-142. Lin Zhi-hong,Chi Hong,Xu Bao-guang.Research of criminal case semantic feature extraction method based on the convolutional neural network[J].Mathematics in Practice and Theory,2017,46(17):129-142.(in Chinese)
[5] Xiao G Y,Mo J Q,Even C,Chen H.Multi-task CNN for classification of Chinese legal questions[A].IEEE 14th International Conference on e-Business Engineering[C].Shanghai,China:IEEE,2017.126-132.
[6] Guo X D,Zhang H L,Ye L,Li S.Learning users' intention of legal consultation through pattern-oriented tensor decomposition with Bi-LSTM[J].Wireless Communications and Mobile Computing,2019,32(3):1-16.
[7] Kolda T G,Bader B W.Tensor decompositions and applications[J].SIAM Review,2009,51(3):455-500.
[8] Chen T,Xu R F,He Y L,Wang X.Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN[J].Expert Systems with Applications,2017,72(4):221-230.
[9] Tucker LR.Some mathematical notes on three-mode factor analysis[J].Psychometrika,1966,31(3):279-311.
[10] Harshman RA.Foundations of the PARAFAC procedure:Models and conditions for an "explanatory" multimodal factor analysis[J].UCLA Working Papers in Phonetics,1970,45(16):1-84.
[11] Vozalis M G,Margaritis K G.Using SVD and demographic data for the enhancement of generalized collaborative filtering[J].Information Sciences,2007,177(15):3017-3037.
[12] Kim Y.Convolutional neural networks for sentence classification[A].Proceedings of the 2014 Conference on empirical methods in natural language processing[C].Doha,Qatar:ACL,2014.1746-1751.
[13] Donahue J,Hendricks L A,Guadarrama S.Long-term recurrent convolutional networks for visual recognition and description[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].Boston,USA:IEEE,2015.2625-2634.
[14] Cho K,Bart V M,Caglar G,Fethi M.Learning phrase representations using RNN encoder-decoder for statistical machine translation[A].Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing[C].Doha,Qatar:ACL,2014.1724-1734.
[15] Donahue J,Hendricks L A,Guadarrama S.Ask the GRU:Multitask learning for deep text recommendations[A].Proceedings of the 10th ACM Conference on Recommender Systems[C].Como,Italy:ACM,2016.107-114.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家自然科学基金 (No.61972079，No.61772126）; 国家重点研发计划 (No.2018YFC0830601）; 教育部基本科研业务费 (No.171802001，No.2016002，No.2016004）; 辽宁省重点研发计划 (No.2019JH2/10100027）; 辽宁省"兴辽英才"计划项目 (No.XLYC1802100）
{{custom_fund}}
PDF(1782 KB)
