莱格纳 (LegNER):一个适合域名的变压器,用于识别法定名称的实体和文本匿名化
Ioannis Karamitsos1, Nikolaos Roufas2, Khalil Al-Hussaeni3
1Department of Graduate Programs and Research, Rochester Institute of Technology, Dubai, United Arab Emirates.
Frontiers in artificial intelligence
|November 24, 2025
概括
我们开发了LegNER,这是一个专门的变压器模型,用于法律命名实体识别 (NER) 和匿名化. 它准确地识别法律实体,并删除敏感信息,提高法律文件处理的效率和隐私.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 法律信息学 法律信息学
背景情况:
- 越来越需要可扩展的,保护隐私的法律文件处理.
- 现有的命名实体识别 (NER) 系统缺乏对法律文本的域特异性.
- 对敏感法律信息进行准确的识别和匿名化的要求.
研究的目的:
- 介绍LegNER,用于法律NER和文本匿名化的域调整的变压器模型.
- 提高处理法律文件的准确性和效率.
- 为法律工作流提供符合GDPR的解决方案.
主要方法:
- 一个BERT基变压器模型的特定领域预训练.
- 培训1542个手动注释的法庭案件,具有广泛的法律词汇.
- 利用跨度级监管来实现可靠的实体认可.
主要成果:
- 对于法律NER,LegNER实现了99%的准确性和99%以上的F1得分.
- 显示高推断效率,每秒处理超过12个文档.
- 成功地为GDPR合规生成连贯的匿名输出.
结论:
- 莱格纳 (LegNER) 的表现明显超过现有的法律NER基线.
- 在法律环境中提供可靠有效的解决方案,用于高精度的实体识别和匿名化.
- 促进自动化的法律分析和符合性敏感的工作流程.
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