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Inf. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            Legal case retrieval aims to automatically scour comparable legal cases based on a given query, which is crucial for offering relevant precedents to support the judgment in intelligent legal systems. Due to similar goals, it is often associated with a similar case matching task. To address them, a daunting challenge is assessing the uniquely defined legal-rational similarity within the judicial domain, which distinctly deviates from the semantic similarities in general text retrieval. Past works either tagged domain-specific factors or incorporated reference laws to capture legal-rational information. However, their heavy reliance on expert or unrealistic assumptions restricts their practical applicability in real-world scenarios. In this article, we propose an end-to-end model named\n            <jats:italic>LCM-LAI<\/jats:italic>\n            to solve the above challenges. Through meticulous theoretical analysis, LCM-LAI employs a dependent multi-task learning framework to capture legal-rational information within legal cases by a law article prediction sub-task, without any additional assumptions in inference. In addition, LCM-LAI proposes an article-aware attention mechanism to evaluate the legal-rational similarity between across-case sentences based on the law distribution, which is more effective than semantic similarity. We perform a series of exhaustive experiments that include two different tasks that involving four real-world datasets. The results demonstrate that LCM-LAI achieves state-of-the-art performance.\n          <\/jats:p>","DOI":"10.1145\/3725729","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T14:56:42Z","timestamp":1742569002000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["How Vital Is the Jurisprudential Relevance: Law Article-Intervened Legal Case Retrieval and Matching"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6902-2572","authenticated-orcid":false,"given":"Nuo","family":"Xu","sequence":"first","affiliation":[{"name":"The MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1434-837X","authenticated-orcid":false,"given":"Pinghui","family":"Wang","sequence":"additional","affiliation":[{"name":"The MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1418-9537","authenticated-orcid":false,"given":"Zi","family":"Liang","sequence":"additional","affiliation":[{"name":"The MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3476-8248","authenticated-orcid":false,"given":"Junzhou","family":"Zhao","sequence":"additional","affiliation":[{"name":"The MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8826-0362","authenticated-orcid":false,"given":"Xiaohong","family":"Guan","sequence":"additional","affiliation":[{"name":"The MOE Key Laboratory for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, China and Tsinghua National Lab for Information Science and Technology, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"477","volume-title":"Proceedings of the European Conference on Information Retrieval (ECIR \u201924)","author":"Askari Arian","year":"2024","unstructured":"Arian Askari, Zihui Yang, Zhaochun Ren, and Suzan Verberne. 2024. 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