{"type":"ScholarlyArticle","title":"Charge Prediction by Constitutive Elements Matching of Crimes","authors":[{"name":"Jie Zhao","slug":"jie-zhao","given":"Jie","family":"Zhao","affiliation":"School of Computer Science and Technology , Xidian University"},{"name":"Ziyu Guan","slug":"ziyu-guan","given":"Ziyu","family":"Guan","affiliation":"School of Computer Science and Technology , Xidian University"},{"name":"Cai Xu","slug":"cai-xu","given":"Cai","family":"Xu","affiliation":"School of Computer Science and Technology , Xidian University"},{"name":"Wei Zhao","slug":"wei-zhao","given":"Wei","family":"Zhao","affiliation":"School of Computer Science and Technology , Xidian University"},{"name":"Enze Chen","slug":"enze-chen","given":"Enze","family":"Chen","affiliation":"School of Computer Science and Technology , Xidian University"}],"abstract":"Charge prediction is to automatically predict the judgemental charges for legal cases. To convict a person/unit of a charge, the case description must contain matching instances of the constitutive elements (CEs) of that charge. This knowledge of CEs is a valuable guide for the judge in making final decisions. However, it is far from fully exploited for charge prediction in the literature. In this paper we propose a novel method named Constitutive Elements-guided Charge Prediction (CECP). CECP mimics human's charge identification process to extract potential instances of CEs and generate predictions accordingly. It avoids laborious labeling of matching instances of CEs by a novel reinforcement learning module which progressively selects potentially matching sentences for CEs and evaluates their relevance. The final prediction is generated based on the selected sentences and their relevant CEs. Experiments on two real-world datasets show the superiority of CECP over competitive baselines.","doi":"10.24963/ijcai.2022/627","url":"https://ijcai.org/proceedings/2022/627","pdf":"https://ijcai.org/proceedings/2022/0627.pdf","markdown":"https://ijcai.org/proceedings/2022/627.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/627","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Natural Language Processing","track":"Main Track","number":627,"pages":{"first":4517,"last":4523},"keywords":["Natural Language Processing: Applications","Natural Language Processing: Text Classification","Natural Language Processing: Information Retrieval and Text Mining","Machine Learning: Deep Reinforcement Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986745","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984888","length_sec":346,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Ashley and Brüninghaus, 2009] Kevin D Ashley and Ste- fanie Brüninghaus. 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