使用模糊逻辑和隐喻识别的BiLSTM增强的法律文本提取模型
1College of Law and Sociology, Qinghai Normal University, Xining, Qinghai, China.
PeerJ. Computer science
|March 10, 2025
概括
本研究介绍了一种使用模糊语言处理和比喻识别用于在线治理的新型法律文本提取模型. 该模型通过分析法律文本有效地识别非法内容,达到高准确度.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 法律信息学 法律信息学
背景情况:
- 自然语言处理 (NLP) 面临的挑战是复杂的法律文本.
- 现有的文本提取方法对于法律领域的细微差别是不够的.
- 在线环境治理需要专门的法律文本分析.
研究的目的:
- 为在线环境治理开发一个开创性的法律文本提取模型.
- 用模糊的语言处理和隐喻识别来解决法律文本的复杂性.
- 改善法律文件中非法内容的识别和分类.
主要方法:
- 使用双向长期短期记忆 (Bi-LSTM) 网络.
- 在Bi-LSTM架构中集成了一个具有L2规范化的自我注意模块.
- 使用模糊语言处理和隐喻识别用于法律文本分析.
主要成果:
- 获得了0.8005.5的宏观F1得分.
- 获得了0.8047的精度和0.8014的回忆.
- 通过将法律条款和判决联系起来,证明有效地划分非法行为.
结论:
- 拟议的法律文本提取模型有效地解决了法律文本识别问题.
- 该模型显示了在线环境治理应用的重大前景.
- 模糊的语言处理和隐喻识别对于分析该领域的法律文本非常有价值.
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