Legal Judgment Prediction Based on Multiclass Information Fusion
Joint Authors
Zhu, Kongfan
Guo, Rundong
Hu, Weifeng
Li, Zeqiang
Li, Yujun
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-26
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination.
In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view.
However, most existing works take the fact description as the sole input for LJP and ignore the external information.
We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination.
We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system.
Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.
American Psychological Association (APA)
Zhu, Kongfan& Guo, Rundong& Hu, Weifeng& Li, Zeqiang& Li, Yujun. 2020. Legal Judgment Prediction Based on Multiclass Information Fusion. Complexity،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1141282
Modern Language Association (MLA)
Zhu, Kongfan…[et al.]. Legal Judgment Prediction Based on Multiclass Information Fusion. Complexity No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1141282
American Medical Association (AMA)
Zhu, Kongfan& Guo, Rundong& Hu, Weifeng& Li, Zeqiang& Li, Yujun. Legal Judgment Prediction Based on Multiclass Information Fusion. Complexity. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1141282
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141282