MFA-LJP: IMPROVED LEGAL JUDGMENT PREDICTION USING BERT EMBEDDINGS-BASED MULTIFRACTAL ANALYSIS

Jian-Hua Guan, Zu-Guo Yu and Xin-Gen Sun
Additional contact information
Jian-Hua Guan: National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, P. R. China2Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, P. R. China
Zu-Guo Yu: National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, P. R. China2Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, P. R. China
Xin-Gen Sun: National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, P. R. China2Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, P. R. China

FRACTALS (fractals), 2025, vol. 33, issue 05, 1-13

Abstract: Legal Judgment Prediction (LJP) is the task of automatically predicting judgment results based on case fact descriptions. However, these fact descriptions can be regarded as lengthy and complex chains of events, and traditional text feature extraction methods struggle to capture the complex multi-scale features from them. To address this, we propose a legal judgment prediction model based on multifractal analysis and BERT embeddings (MFA-LJP). In this work, we treat the [CLS] embeddings from BERT as one-dimensional time series and perform multifractal analysis to reveal the multifractal properties that these time series exhibit. Furthermore, for each time series, we extract multifractal parameters and concatenate them with the corresponding output from the BiGRU to create a new feature vector. Finally, this feature vector is used as the input to a fully connected layer for the prediction tasks of applicable law articles, charges, and terms of penalty, respectively. Experiments on CAIL datasets demonstrate the effectiveness of MFA. With only 11 multifractal parameters added, MFA-LJP can achieve considerable performance improvement in three sub-tasks. Compared to the current best baseline model HD-LJP, on the CAIL-small dataset, MFA-LJP improves the F1 scores for applicable law articles, charges, and terms of penalty by 5.63%, 2.54%, and 2.95%, respectively. Similarly, on the CAIL-big dataset, MFA-LJP also shows notable achievements, with F1 scores for applicable law articles, charges, and terms of penalty improving by 3.08%, 4.33%, and 8.91%, respectively.

Keywords: Legal Judgment Prediction; Multifractal Analysis; BERT Embedding (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X25500422
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:33:y:2025:i:05:n:s0218348x25500422

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X25500422

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().