research-article

Hierarchical Matching Network for Crime Classification

Authors:

Yongfeng Zhang,

Jiafeng GuoAuthors Info & Claims

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 325 - 334

https://doi.org/10.1145/3331184.3331223

Published: 18 July 2019 Publication History

Abstract

Automatic crime classification is a fundamental task in the legal field. Given the fact descriptions, judges first determine the relevant violated laws, and then the articles. As laws and articles are grouped into a tree-shaped hierarchy (i.e., laws as parent labels, articles as children labels), this task can be naturally formalized as a two layers' hierarchical multi-label classification problem. Generally, the label semantics (i.e., definition of articles) and the hierarchical structure are two informative properties for judges to make a correct decision. However, most previous methods usually ignore the label structure and feed all labels into a flat classification framework, or neglect the label semantics and only utilize fact descriptions for crime classification, thus the performance may be limited. In this paper, we formalize crime classification problem into a matching task to address these issues. We name our model as Hierarchical Matching Network (HMN for short). Based on the tree hierarchy, HMN explicitly decomposes the semantics of children labels into the residual and alignment components. The residual components keep the unique characteristics of each individual children label, while the alignment components capture the common semantics among sibling children labels, which are further aggregated as the representation of their parent label. Finally, given a fact description, a co-attention metric is applied to effectively match the relevant laws and articles. Experiments on two real-world judicial datasets demonstrate that our model can significantly outperform the state-of-the-art methods.

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Cited By

Sheik RNamani CPrasanna PNirmala S(2025)Enhancing Rhetorical Role Identification in Legal Documents Using Large Language Models and IN_place Data AugmentationComputing, Communication and Learning10.1007/978-3-031-79041-6_8(90-100)Online publication date: 1-Feb-2025
https://doi.org/10.1007/978-3-031-79041-6_8
Paul SBhatt RGoyal PGhosh SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Legal Statute Identification: A Case Study using State-of-the-Art Datasets and MethodsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657879(2231-2240)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657879
Yue LLiu QJin BWu HAn Y(2024)A Circumstance-Aware Neural Framework for Explainable Legal Judgment PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338758036:11(5453-5467)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3387580
Show More Cited By

Index Terms

Hierarchical Matching Network for Crime Classification
1. Applied computing
  1. Law, social and behavioral sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches

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cover image ACM Conferences

SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2019

1512 pages

ISBN:9781450361729

DOI:10.1145/3331184

General Chairs:
Benjamin Piwowarski
CNRS - Sorbonne Universite, France
,
Max Chevalier
Universite de Toulouse, CNRS, France
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Eric Gaussier
Universite Grenoble Alpes, CNRS, France
,
Program Chairs:
Yoelle Maarek
Amazon Research, Israel
,
Jian-Yun Nie
University of Montreal, Canada
,
Falk Scholer
RMIT University, Australia

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Published: 18 July 2019

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the fundamental Research for the Central Universities
National Natural Science Foundation of China

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SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

July 21 - 25, 2019

Paris, France

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Sheik RNamani CPrasanna PNirmala S(2025)Enhancing Rhetorical Role Identification in Legal Documents Using Large Language Models and IN_place Data AugmentationComputing, Communication and Learning10.1007/978-3-031-79041-6_8(90-100)Online publication date: 1-Feb-2025
https://doi.org/10.1007/978-3-031-79041-6_8
Paul SBhatt RGoyal PGhosh SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Legal Statute Identification: A Case Study using State-of-the-Art Datasets and MethodsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657879(2231-2240)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657879
Yue LLiu QJin BWu HAn Y(2024)A Circumstance-Aware Neural Framework for Explainable Legal Judgment PredictionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338758036:11(5453-5467)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2024.3387580
Le YQuan ZWang JCao DLi K(2024)$\boldsymbol{R}^{2}$: A Novel Recall & Ranking Framework for Legal Judgment PredictionIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2024.336538932(1609-1622)Online publication date: 19-Feb-2024
https://dl.acm.org/doi/10.1109/TASLP.2024.3365389
Wei CZhang QXu JWang YHuang ZHao T(2024)Legal Judgement Prediction via Contrastive Learning Based-Retrieval and Semantic Embedding2024 IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA)10.1109/ISPCE-ASIA64773.2024.10756244(1-6)Online publication date: 25-Oct-2024
https://doi.org/10.1109/ISPCE-ASIA64773.2024.10756244
Zhang YWei XYu H(2024)HD-LJPKnowledge-Based Systems10.1016/j.knosys.2024.112033299:COnline publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112033
Feng JZhao LQin HXu YWang Z(2024)CADLRA: A multi-charge prediction method based on the Criminal Act-Driven Law Retrieval AugmentationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108619134(108619)Online publication date: Aug-2024
https://doi.org/10.1016/j.engappai.2024.108619
Mishra SAhmed TMishra VSrivastava PSayeed AGupta U(2024)Rhetorical Role Detection in Legal Judgements Using Zero-Shot LearningProceedings of Data Analytics and Management10.1007/978-981-99-6544-1_42(559-574)Online publication date: 14-Jan-2024
https://doi.org/10.1007/978-981-99-6544-1_42
Srivastav APrajapat S(2024)A Decision Tree Approach for Identifying Indian Penal Code Sections Across Different Crime AspectsContributions Presented at The International Conference on Computing, Communication, Cybersecurity and AI, July 3–4, 2024, London, UK10.1007/978-3-031-74443-3_45(773-782)Online publication date: 20-Dec-2024
https://doi.org/10.1007/978-3-031-74443-3_45
Luo DCheng HLi QXu HElkind E(2023)Coupled point process-based sequence modeling for privacy-preserving network alignmentProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/678(6112-6120)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/678
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