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CODE-ACCORD: 构建规则生成的监管数据库,用于实现自动合规检查
Hansi Hettiarachchi1, Amna Dridi2, Mohamed Medhat Gaber2
1Faculty of Science and Technology, Lancaster University, Lancaster, LA1 4WA, UK. h.hettiarachchi@lancaster.ac.uk.
Scientific data
|January 29, 2025
概括
我们推出CODE-ACCORD,这是一个用于建筑,工程和建筑 (AEC) 部门自动合规检查 (ACC) 的新数据集. 该资源有助于将复杂的建筑法规转换为使用机器学习的机器可读格式.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 建筑,工程和建筑 (AEC) 中的自动合规检查 (ACC) 需要对建筑法规进行自动解释.
- 挑战包括自然语言的复杂性和机器学习 (ML) 的有限资源,以将文本规则转换为机器可读的格式.
研究的目的:
- 为ACC解决自动化建筑法规解释方面的挑战.
- 介绍CODE-ACCORD,一个用于机器可读规则生成的手动注释数据集.
主要方法:
- 收集了来自英语和芬兰建筑法规的862个独立句子.
- 由12名注释者手动注释的实体和关系,其次是策划.
- 开发了一个数据集,包含4,297个实体和4,329个关系作为基本真相.
主要成果:
- CODE-ACCORD 数据集为机器学习任务提供了强大的基础真相.
- 该数据集包含4,297个实体和4,329个不同类别的关系.
- 促进ML和自然语言处理 (NLP) 应用程序,如文本分类,实体识别和关系提取.
结论:
- CODE-ACCORD 能够将先进的机器学习技术 (包括深度神经网络和大型语言模型) 应用到 ACC.
- 该数据集对于在AEC行业内推进自动化合规检查的研发至关重要.
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