Statute recommendation: Re-ranking statutes by modeling case-statute relation with interpretable hand-crafted features


In the continental law system, it is appropriate for judges to find relevant laws and consider rules defined in them when dealing with legal cases. Therefore, recommending relevant laws quickly and accurately based on case content is crucial in improving the efficiency of case processing. There have been researched works of recommender systems in various fields, but few of them lucubrates systems that recommend statutes for cases. To the best of our knowledge, there is no research on recommending statutes by modeling the relationship between case content and law content with interpretable hand-crafted features. In this paper, we define five novel types of features for calculating relevance between a case and a statute for resorting all statutes retrieved through collaborative filtering for the input case. Both pair-wise and list-wise ranking models are trained based on all these features for re-ranking the statutes list. Besides, we also test the combinations of different learning algorithms and popular pre-trained language models. Experimental results show that adopting the proposed novel features in pair-wise ranking achieves the best performance. It improves the recommendation recall of the Top 1 statute by almost 5% compared with the collaborative filtering approach.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: © 2022, The Authors. Published by Elsevier. Licensed under the Creative Commons Attribution 4.0 International
Publication ISSN: 1872-6291
Last Modified: 19 Jun 2024 17:27
Date Deposited: 05 Aug 2022 10:26
Full Text Link:
Related URLs: https://www.sci ... 6363?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2022-08
Published Online Date: 2022-06-14
Accepted Date: 2022-06-11
Authors: Li, Chuanyi
Ge, Jidong
Cheng, Kun
Luo, Bin
Chang, Victor (ORCID Profile 0000-0002-8012-5852)



Version: Published Version

License: Creative Commons Attribution

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