通过选择性推断从法律决策中发现重要主题
1Yong Pung How School of Law, Singapore Management University School, Singapore.
概括
本研究引入了一个自动化管道,以找到与案件结果相关的关键法律主题. 该方法有助于分析法律文本,并确定重要的案例主题,以获得更好的法律见解.
科学领域:
- 计算社会科学 计算社会科学
- 法律信息学 法律信息学
- 自然语言处理自然语言处理.
背景情况:
- 传统的法律分析通常依赖于手动解释,这可能是耗时和主观的.
- 在大量法律文本中发现重要的主题对于理解司法推理和结果至关重要.
- 自动化方法可以提高法律文本分析的效率和客观性.
研究的目的:
- 开发和评估一个自动化管道,用于识别法律决策文本中的重要主题.
- 将已识别的主题与案例结果相关联,并提供可解释的主题-词语分布.
- 在各种法律案例数据集上展示管道的实用性.
主要方法:
- 使用主题模型 (隐性语义分析,语言模型嵌入) 来合成法律文本的特征.
- 应用处罚回归和选择后显著性测试来识别统计学上重要的主题.
- 开发案例主题权重,以识别特定主题内的代表性案例.
主要成果:
- 该管道成功地确定了与域名纠纷和欧洲人权法院案件案件结果有显著关联的主题.
- 生成的主题词的分布是可解释的,并与既定的法律学说保持一致.
- 案例主题权重有助于识别特定法律主题的代表案例.
结论:
- 自动化管道为发现法律决策文本中重要主题提供了强大而有效的方法.
- 确定的主题为司法决策提供了有价值的见解,并可以支持各种法律分析任务.
- 这种方法将计算方法与法律学术相结合,为法律和治理领域做出贡献.
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