基于Bi-LSTM的司法论监督和智能决策模型的设计
1College of Humanities, Law and Foreign Languages, Taiyuan University of Technology, Taiyuan, Shanxi, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一种新的模型,用于监测司法领域的网络论 (NPO),使用语义分析和Bi-LSTM网络. 该模型增强了司法当局管理NPO的能力,支持司法独立.
科学领域:
- 智能决策支持系统 智能决策支持系统
- 模糊逻辑和偏好建模模的模糊逻辑和偏好建模
- 自然语言处理用于社交媒体分析
背景情况:
- 网络公众论 (NPO) 影响司法改革,但可能威胁司法独立.
- 现有的NPO监控技术面临着大数据集和干扰的挑战.
- 智能决策支持系统需要强大的方法来分析公众情绪.
研究的目的:
- 提供当前NPO监控技术的概述.
- 提出一个新的司法领域的NPO监测模型.
- 加强司法当局在管理NPO方面的能力.
主要方法:
- 开发了一个基于语义特征分析的司法领域NPO监控模型.
- 包含时间序列特征,二进制语义匹配,以及公众情绪强度.
- 使用双向长短期内存 (Bi-LSTM) 网络进行语义相似性计算.
主要成果:
- 拟议的S-Bi-LSTM模型在测试组中实现了85.9%的准确性和87.1%的F1得分.
- 在经验评估中表现优于现有的句子语义相似性模型.
- 在监测司法NPO方面显著提高.
结论:
- 这种新型模式有效地解决了监控司法NPO的挑战.
- 改进的监控能力减轻了司法机构的公共关系负担.
- 通过管理公众论,促进司法权的更公平的执行.
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