Existing automated compliance checking (ACC) systems require the extraction of requirements from regulatory documents into computer-processable representations. These information extraction (IE) processes are either fully manual, semi-automated, or automated. Semi-automated and manual approaches typically use manual annotations or predefined IE rules, which lack sufficient flexibility and scalability; the annotations and rules typically need adaptation if the characteristics of the regulatory document change. There is, thus, a need for a fully automated IE approach that can achieve high and consistent performance across different types of regulatory documents for supporting ACC. To address this need, this paper proposes a deep neural network-based method for deep IE – extracting semantic and syntactic information elements – from regulatory documents in the architectural, engineering, and construction (AEC) domain. The proposed method was evaluated in extracting information from multiple regulatory documents in the AEC domain. It achieved average precision and recall of 93.1% and 92.9%, respectively.

现有的自动合规检查 (ACC) 系统需要将监管文件中的要求提取到计算机可处理的表示中。这些信息提取 (IE) 过程是完全手动、半自动或自动的。半自动和手动方法通常使用手动注释或预定义的 IE 规则，缺乏足够的灵活性和可扩展性；如果规范性文件的特征发生变化，注释和规则通常需要进行调整。因此，需要一种完全自动化的 IE 方法，该方法可以在支持 ACC 的不同类型的监管文件中实现高且一致的性能。为了解决这个需求，本文提出了一种基于深度神经网络的深度 IE 方法——从建筑、工程和施工 (AEC) 领域的监管文件中提取语义和句法信息元素。在从 AEC 领域的多个监管文件中提取信息时，对所提出的方法进行了评估。它分别达到了 93.1% 和 92.9% 的平均准确率和召回率。