使用来自变压器的双向编码器表示 (BERT) 来预测台湾法院判决中的刑事指控和判决
Yi-Ting Peng1, Chin-Laung Lei1
1Department of Electrical Engineering, National Taiwan University, Taipei City, Taiwan.
PeerJ. Computer science
|March 4, 2024
概括
本研究使用来自变压器的双向编码器表示 (BERT) 来从台湾法院数据中预测刑事指控和刑期. 该模型实现了高精度,证明了法律文本分析的可行性.
科学领域:
- 计算语言学 计算语言学
- 法律信息学 法律信息学
- 人工智能的人工智能
背景情况:
- 对于那些不熟悉法律的人来说,理解法律法规和预测犯罪行为的结果可能是一个挑战.
- 刑事司法系统产生大量的文本数据,如法庭判决,这些数据包含有价值的预测信息.
研究的目的:
- 开发和评估一种机器学习模型,以利用台湾地区法院的判决来预测刑事指控和刑期.
- 为分析法律文本和克服其固有的局限性,如512令牌限制,调整和改进双向编码器从变压器表示 (BERT) 模型.
主要方法:
- 利用来自地区法院的台湾刑事判决数据集.
- 应用并微调了双向编码器从变压器 (BERT) 的表示模型的两个主要任务:刑事指控预测和句子长度预测.
- 开发了一种新的解决方案,以解决BERT的512令牌输入限制,用于处理冗长的法律文件.
主要成果:
- 在预测刑事指控方面达到98.95%的高准确度.
- 用BERT来分析台湾刑事判决的可行性.
- 获得了对句子长度预测的有希望的准确率:72.37%的伤害试验和80.93%的公共危害试验.
结论:
- 伯特模型是分析法律文本和预测台湾刑事司法系统结果的可行工具.
- 拟议的修改有效地解决了BERT在广泛的法律文件上应用时的局限性.
- 这项研究为AI驱动的法律分析和决策支持系统铺平了道路.
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