中国法律领域的生成性命名实体认可框架
Xingliang Mao1, Jie Jiang2, Yongzhe Zeng3
1School of Digital Media Engineering and Humanities, Hunan University of Technology and Business, Changsha, Hunan, China.
PeerJ. Computer science
|December 9, 2024
概括
本研究引入了一个新的法律实体名称认可 (NER) 框架,可以提高识别复杂法律实体的准确性. 该方法增强了实体类型预测和法律文本中的边界检测.
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:
- 命名实体识别 (NER) 在NLP中至关重要,但由于复杂的实体结构,在法律领域具有挑战性.
- 目前的NER方法在准确定义法律实体的界限和类型方面遇到了困难.
- 法律领域需要专门的方法来有效地提取信息.
研究的目的:
- 为法律命名实体识别 (NER) 开发一个新的序列对序列框架.
- 提高识别复杂法人及其类型的准确性.
- 克服法律文本分析现有方法的局限性.
主要方法:
- 一个新的序列到序列框架,包含一个实体类型意识模块.
- 利用对比式学习来改善实体类型预测.
- 实现具有复制机制的解码器,用于准确识别法人.
主要成果:
- 拟议的框架在两个法律数据集上显著优于最先进的方法.
- 在准确性,回忆力和法律NER的F1得分方面取得了显著的改进.
- 证明有效识别复杂的法律实体,没有明确的标记方案.
结论:
- 开发的框架在法律命名实体认可方面取得了重大进展.
- 这种方法对未来在自动化法律文本分析方面的研究有希望.
- 在法律文本中承认有效实体对于各种法律应用至关重要.
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