Addressing long-tailed distribution in judicial text for criminal motive classification: a balanced contrastive learning approach

Addressing long-tailed distribution in judicial text for criminal motive classification: a balanced contrastive learning approach

Abstract Understanding criminal motives is crucial for analyzing criminal psychology and predicting judicial outcomes. Traditional methods for crime motive analysis are heavily based on statistical techniques, requiring specialized knowledge and substantial human resources. With the increasing avail...

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Bibliographic Details
Main Authors: Ting Li, Lewen Mi, Xiangyu Meng, Yongju Jia, Lin Zhao, Qi Zhao, Zihao Wei, Guandong Gao, Xiangxian Li
Format: Article
Language:English
Published: SpringerOpen 2025-02-01
Series:EPJ Data Science
Subjects:
Criminal motive
Text classification
Long-tailed distribution
Contrastive learning
Online Access:https://doi.org/10.1140/epjds/s13688-025-00533-1
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https://doi.org/10.1140/epjds/s13688-025-00533-1

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