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适用于预测无物质损害赔偿的法律判决的回归
Thiago Raulino Dal Pont1, Isabela Cristina Sabo2, Jomi Fred Hübner1
1Department of Automation and Systems, Universidade Federal de Santa Catarina, Florianopolis, Santa Catarina, Brazil.
使用文本回归来预测非物质损害赔偿,可以帮助各方提前达成协议. 包括专家提取的属性在内的NLP和ML技术提高了法律判决中的预测准确性.
科学领域:
- 计算语言学 计算语言学
- 法律信息学 法律信息学
- 机器学习 机器学习
背景情况:
- 无物质损害赔偿缺乏明确的评估标准,导致司法主观性.
- 早期估计赔偿金可能有助于解决法律纠纷.
研究的目的:
- 调查文本回归用于预测航空公司消费者案件中的无物质损害赔偿.
- 评估各种自然语言处理 (NLP) 和机器学习 (ML) 调整对预测准确性和效率的影响.
主要方法:
- 开发了文本回归管道,包括N-Grams提取,特征选择,超拟合避免,交叉验证和异常值删除等调整.
- 引入了专家衍生属性添加 (AELE) 作为一个补充输入.
- 根据预测质量和执行时间评估管道性能.
主要成果:
- N-Grams提取和AELE显著提高了预测质量.
- 功能选择和过度装配 避免影响执行时间.
- 有时具有调整子集的管道表现优于具有所有调整的管道.
- 最好的管道在法律背景下实现了可接受的预测错误.
结论:
- 文本回归模型,特别是具有特定的NLP/ML调整和专家输入的文本回归模型,对于估计非物质损害赔偿有希望.
- 优化管道可以提供有价值的预测,以帮助法律和解.
- 进一步的研究可以为更广泛的法律应用改进这些模型.
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