Abstract
This study explores the integration of Building Information Modeling (BIM) with Natural Language Processing (NLP) to automate the extraction of requirements from unstructured French Building Technical Specification (BTS) documents within the construction industry. Employing Named Entity Recognition (NER) and Relation Extraction (RE) techniques, the study leverages the transformer-based model CamemBERT and applies transfer learning with the French language model Fr\_core\_news\_lg, both pre-trained on a large French corpus in the general domain. To benchmark these models, additional approaches ranging from rule-based to deep learning-based methods are developed. For RE, four different supervised models, including Random Forest, are implemented using a custom feature vector. A hand-crafted annotated dataset is used to compare the effectiveness of NER approaches and RE models. Results indicate that CamemBERT and Fr\_core\_news\_lg exhibited superior performance in NER, achieving F1-scores over 90\%, while Random Forest proved most effective in RE, with an F1 score above 80\%. The outcomes are intended to be represented as a knowledge graph in future work to further enhance automatic verification systems.
Abstract (translated)
这项研究探讨了将建筑信息模型（BIM）与自然语言处理（NLP）相结合，以自动化提取法国建筑工程技术规范（BTS）文档中的需求。这些文档是未结构化的，并且是在建筑业中使用的。该研究利用命名实体识别（NER）和关系抽取（RE）的技术手段，采用基于变换器的CamemBERT模型，并使用法语语言模型Fr_core_news_lg进行迁移学习，后者在广泛的通用领域大型语料库上进行了预训练。为了对这些模型进行基准测试，开发了从规则基础方法到深度学习方法的一系列额外方法。 对于关系抽取（RE），采用了一个自定义特征向量，实现了包括随机森林在内的四种不同的监督式模型。使用手工注释的数据集来比较命名实体识别（NER）的方法和关系抽取（RE）的模型的有效性。结果表明，在命名实体识别方面，CamemBERT和Fr_core_news_lg表现出色，F1分数超过90%；而在关系抽取方面，随机森林方法最为有效，其F1分数超过了80%。 研究结果旨在未来的进一步工作中以知识图谱的形式表示出来，从而增强自动验证系统。
URL
https://arxiv.org/abs/2508.13833
