Computer Science ›› 2025, Vol. 52 ›› Issue (2): 222-230.doi: 10.11896/jsjkx.240600081

• Artificial Intelligence • Previous Articles     Next Articles

Case Element Association with Evidence Extraction for Adjudication Assistance

LIU Yanlun, XIAO Zheng, NIE Zhenyu, LE Yuquan, LI Kenli   

  1. College of Information Science and Engineering,Hunan University,Changsha 410082,China
  • Received:2024-06-12 Revised:2024-09-06 Online:2025-02-15 Published:2025-02-17
  • About author:LIU Yanlun,born in 1997,postgra-duate.His main research interests include natural language processiong and so on.
    XIAO Zheng,born in 1981,professor,is a member of CCF(No.16339M).His main research interests include high performance computing and parallel distributed systems.
  • Supported by:
    National Key R&D Program of China(2022YFC3303403).

Abstract: Researchers in the past have devoted themselves to finding similar cases through the method of case matching.But the case-matching method depends on the text similarity.Similarity of texts is not equal to similarity of cases.Moreover,case ma-tching lacks interpretability.To address the shortcomings of case matching,we define a new problem,case element association with evidence extraction,which aims to predict the association results by elements rather than text similarity,and extracts factual details as evidence to explain the association result.This new problem is more in line with the actual needs of legal practitioners.In order to make the proposed model perform better on this new problem,contrastive learning is introduced to solve the problem of over-dependence on direct expressions of elements,which makes the attention weights evenly distributed on different expressions of same elements,thereby improving the effect of our model.We perform experiments on public and self-constructed datasets.Experiment results show that compared with text matching models,the proposed model improves the accuracy and precision by about 20%,and improves the recall and F1 by about 30%.

Key words: Contrastive learning, Case association, Attention mechanism, Pretrained language model, Natural language processing

CLC Number: 

  • TP391
[1]WANG J L,WU Y L.Research on the application of similar case retrieval system in judicial practice[J].Legality Vision,2022(2):100-102.
[2]YU W,SUN Z,XU J,et al.Explainable legal case matching via inverse optimal transport-based rationale extraction[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.2022:657-668.
[3]LIMSOPATHAM N.Effectively leveraging BERT for legal do-cument classification[C]//Proceedings of the Natural Legal Language Processing Workshop 2021.2021:210-216.
[4]CHALKIDIS I,FERGADIOTIS M,MALAKASIOTIS P,et al.LEGAL-BERT:The Muppets straight out of Law School[C]//Findings of the Association for Computational Linguistics:EMNLP 2020.2020:2898-2904.
[5]ZHENG L,GUHA N,ANDERSON B R,et al.When does pretraining help? assessing self-supervised learning for law and the casehold dataset of 53,000+ legal holdings[C]//Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law.2021:159-168.
[6]PAUL S,MANDAL A,GOYAL P,et al.Pre-trained languagemodels for the legal domain:a case study on Indian law[C]//Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law.2023:187-196.
[7]KRASADAKIS P,SAKKOPOULOS E,VERYKIOS V S.Asurvey on challenges and advances in natural language processing with a focus on legal informatics and low-resource languages[J].Electronics,2024,13(3):648.
[8]LI H,AI Q,CHEN J,et al.SAILER:structure-aware pre-trained language model for legal case retrieval[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.2023:1035-1044.
[9]SONG D,GAO S,HE B,et al.On the effectiveness of pre-trained language models for legal natural language processing:An empirical study[J].arXiv.2204.03498,2022.
[10]CHALKIDIS I,GARNEAU N,GOANTA C,et al.LeXFiles and LegalLAMA:Facilitating English Multinational Legal Language Model Development[C]//The 61st Annual Meeting of the Association for Computational Linguistics.2023.
[11]PENG D,YANG J,LU J.Similar case matching with explicitknowledge-enhanced text representation[J].Applied Soft Computing,2020,95:106514.
[12]FANG J,LI X,LIU Y.Low-Resource Similar Case Matching in Legal Domain[C]//International Conference on Artificial Neural Networks.Cham:Springer Nature Switzerland,2022:570-582.
[13]BHATTACHARYA P,GHOSH K,PAL A,et al.Legal casedocument similarity:You need both network and text [J].Information Processing & Management,2022,59(6):103069.
[14]BIBAL A,LOGNOUL M,DE STREEL A,et al.Legal requirements on explainability in machine learning[J].Artificial Intelligence and Law,2021,29:149-169.
[15]ZHONG H,WANG Y,TU C,et al.Iteratively questioning and answering for interpretable legal judgment prediction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:1250-1257.
[16]SHU Y,ZHAO Y,ZENG X,et al.Cail2019-fe:Tech.Rep.[R].2019.
[17]LIU H S,WANG L,SUN Y Y,et al.Case element recognition method based on pre-trained language model[J] Journal of Chinese Information Processing,2021,35(11):91-100.
[18]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Volume 1(Long and Short Papers).2019:4171-4186.
[19]SHEN Z,LIU Z,LIU Z,et al.Un-mix:Rethinking image mixtures for unsupervised visual representation learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:2216-2224.
[20]WANG T,ISOLA P.Understanding contrastive representation learning through alignment and uniformity on the hypersphere[C]//International conference on machine learning.PMLR,2020:9929-9939.
[21]YAN Y,LI R,WANG S,et al.A contrastive framework for self-supervised sentence representation transfer[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing.2021.
[22]GAO T,YAO X,CHEN D.SimCSE:Simple Contrastive Lear-ning of Sentence Embeddings[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Proces-sing.Association for Computational Linguistics,2021.
[23]NGUYEN H T,PHI M K,NGO X B,et al.Attentive deep neural networks for legal document retrieval[J].Artificial Intelligence and Law,2024,32(1):57-86.
[24]XIAO C,HU X,LIU Z,et al.Lawformer:A pre-trained lan-guage model for chinese legal long documents[J].AI Open,2021,2:79-84.
[25]SHAO Y,MAO J,LIU Y,et al.BERT-PLI:Modeling para-graph-level interactions for legal case retrieval[C]//IJCAI.2020:3501-3507.
[26]CAO F X,SUN Y Y,WANG Z Z,et al.Similar case matching model for loan cases[J].Computer Engineering,2024,50(1):306-312.
[27]CHEN C,LI K,TEO S G,et al.Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks[J].ACM Transactions on Knowledge Discovery from Data(TKDD),2020,14(4):1-23.
[28]LIU C,LI K,LI K,et al.A new service mechanism for profit optimizations of a cloud provider and its users[J].IEEE Transactions on Cloud Computing,2017,9(1):14-26.
[29]TALEB T,KSENTINI A,FRANGOUDIS P A.Follow-mecloud:When cloud services follow mobile users[J].IEEE Transactions on Cloud Computing,2016,7(2):369-382.
[30]SHAO Y,WU Y,LIU Y,et al.Investigating user behavior in legal case retrieval[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:962-972.
[31]LIU B,WU Y,LIU Y,et al.Conversational vs traditional:Comparing search behavior and outcome in legal case retrieval[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.2021:1622-1626.
[32]MINOCHA A,SINGH N,SRIVASTAV A A.Finding relevant indian judgments using dispersion of citation network[C]//Proceedings of the 24th international conference on World Wide Web.2015:1085-1088.
[33]TIAN Y,SUN C,POOLE B,et al.What makes for good views for contrastivelearning?[J].Advances in Neural Information Processing Systems,2020,33:6827-6839.
[34]GUO J,FAN Y,JI X,et al.Matchzoo:A learning,practicing,and developing system for neural text matching[C]//Procee-dings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.2019:1297-1300.
[35]HU B,LU Z,LI H,et al.Convolutional neural network architectures for matching natural language sentences[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2.2014:2042-2050.
[36]TALMAN A,YLI-JYRÄ A,TIEDEMANN J.Sentence embeddings in NLI with iterative refinement encoders[J].Natural Language Engineering,2019,25(4):467-482.
[37]WAN S,LAN Y,GUO J,et al.A deep architecture for semantic matching with multiple positional sentence representations[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2016.
[38]WAN S,LAN Y,XU J,et al.Match-SRNN:modeling the recursive matching structure with spatial RNN[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.2016:2922-2928.
[39]WANG Z,HAMZA W,FLORIAN R.Bilateral multi-perspective matching for natural language sentences[C]//International Joint Conference on Artificial Intelligence.2017.
[40]CHEN Q,ZHU X,LING Z H,et al.Enhanced LSTM for Natural Language Inference[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Vo-lume 1:Long Papers).Association for Computational Linguistics,2017.
[41]DAI Z,XIONG C,CALLAN J,et al.Convolutional neural networks for soft-matching n-grams in ad-hoc search[C]//Procee-dings of the Eleventh ACM International Conference on Web Search and Data Mining.2018:126-134.
[42]HUANG P S,HE X,GAO J,et al.Learning deep structured semantic models for web search using clickthrough data[C]//Proceedings of the 22nd ACM international conference on Information & Knowledge Management.2013:2333-2338.
[43]PARANJAPE B,JOSHI M,THICKSTUN J,et al.An Information Bottleneck Approach for Controlling Conciseness in Ratio-nale Extraction[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP).Association for Computational Linguistics,2020.
[44]JAIN S,WIEGREFFE S,PINTER Y,et al.Learning to faithfully rationalize by construction[C]//58th Annual Meeting of the Association for Computational Linguistics(ACL 2020).2020:4459-4473.
[45]PRUTHI D,DHINGRA B,NEUBIG G,et al.Weakly-and Semi-supervised Evidence Extraction[C]//Findings of the Association for Computational Linguistics:EMNLP 2020.2020:3965-3970.
[46]LI D,HU B,CHEN Q,et al.Unifying model explainability and robustness for joint text classification and rationale extraction[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2022:10947-10955.
[1] LI Daicheng, LI Han, LIU Zheyu, GONG Shiheng. MacBERT Based Chinese Named Entity Recognition Fusion with Dependent Syntactic Information and Multi-view Lexical Information [J]. Computer Science, 2025, 52(6A): 240600121-8.
[2] HUANG Bocheng, WANG Xiaolong, AN Guocheng, ZHANG Tao. Transmission Line Fault Identification Method Based on Transfer Learning and Improved YOLOv8s [J]. Computer Science, 2025, 52(6A): 240800044-8.
[3] WU Zhihua, CHENG Jianghua, LIU Tong, CAI Yahui, CHENG Bang, PAN Lehao. Human Target Detection Algorithm for Low-quality Laser Through-window Imaging [J]. Computer Science, 2025, 52(6A): 240600069-6.
[4] ZENG Fanyun, LIAN Hechun, FENG Shanshan, WANG Qingmei. Material SEM Image Retrieval Method Based on Multi-scale Features and Enhanced HybridAttention Mechanism [J]. Computer Science, 2025, 52(6A): 240800014-7.
[5] HOU Zhexiao, LI Bicheng, CAI Bingyan, XU Yifei. High Quality Image Generation Method Based on Improved Diffusion Model [J]. Computer Science, 2025, 52(6A): 240500094-9.
[6] DING Xuxing, ZHOU Xueding, QIAN Qiang, REN Yueyue, FENG Youhong. High-precision and Real-time Detection Algorithm for Photovoltaic Glass Edge Defects Based onFeature Reuse and Cheap Operation [J]. Computer Science, 2025, 52(6A): 240400146-10.
[7] WANG Rong , ZOU Shuping, HAO Pengfei, GUO Jiawei, SHU Peng. Sand Dust Image Enhancement Method Based on Multi-cascaded Attention Interaction [J]. Computer Science, 2025, 52(6A): 240800048-7.
[8] LI Jianghui, DING Haiyan, LI Weihua. Prediction of Influenza A Antigenicity Based on Few-shot Contrastive Learning [J]. Computer Science, 2025, 52(6A): 240800053-6.
[9] WANG Baohui, GAO Zhan, XU Lin, TAN Yingjie. Research and Implementation of Mine Gas Concentration Prediction Algorithm Based on Deep Learning [J]. Computer Science, 2025, 52(6A): 240400188-7.
[10] ZHENG Chuangrui, DENG Xiuqin, CHEN Lei. Traffic Prediction Model Based on Decoupled Adaptive Dynamic Graph Convolution [J]. Computer Science, 2025, 52(6A): 240400149-8.
[11] HONG Yi, SHEN Shikai, SHE Yumei, YANG Bin, DAI Fei, WANG Jianxiao, ZHANG Liyi. Multivariate Time Series Prediction Based on Dynamic Graph Learning and Attention Mechanism [J]. Computer Science, 2025, 52(6A): 240700047-8.
[12] TENG Minjun, SUN Tengzhong, LI Yanchen, CHEN Yuan, SONG Mofei. Internet Application User Profiling Analysis Based on Selection State Space Graph Neural Network [J]. Computer Science, 2025, 52(6A): 240900060-8.
[13] ZHAO Chanchan, YANG Xingchen, SHI Bao, LYU Fei, LIU Libin. Optimization Strategy of Task Offloading Based on Meta Reinforcement Learning [J]. Computer Science, 2025, 52(6A): 240800050-8.
[14] GUAN Xin, YANG Xueyong, YANG Xiaolin, MENG Xiangfu. Tumor Mutation Prediction Model of Lung Adenocarcinoma Based on Pathological [J]. Computer Science, 2025, 52(6A): 240700010-8.
[15] TAN Jiahui, WEN Chenyan, HUANG Wei, HU Kai. CT Image Segmentation of Intracranial Hemorrhage Based on ESC-TransUNet Network [J]. Computer Science, 2025, 52(6A): 240700030-9.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!