# Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling

arXiv: https://arxiv.org/abs/2508.13833 · HTML: https://mlsanity.com/paper/2508.13833/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 88 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [CamemBERT: a Tasty French Language Model](https://mlsanity.com/paper/1911.03894/) (2019)
- [FlauBERT: Unsupervised Language Model Pre-training for French](https://mlsanity.com/paper/1912.05372/) (2019)
- [The Learnability of In-Context Learning](https://mlsanity.com/paper/2303.07895/) (2023)
- [Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction](https://mlsanity.com/paper/1911.09886/) (2019)
- [Distant Supervision for Relation Extraction beyond the Sentence Boundary](https://mlsanity.com/paper/1609.04873/) (2016)
- [word2vec Parameter Learning Explained](https://mlsanity.com/paper/1411.2738/) (2014)
- [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015)
- [Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path](https://mlsanity.com/paper/1508.03720/) (2015)
- [Robust Distant Supervision Relation Extraction via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.09927/) (2018)
- [Span Model for Open Information Extraction on Accurate Corpus](https://mlsanity.com/paper/1901.10879/) (2019)
- [Multi2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT](https://mlsanity.com/paper/2009.08128/) (2020)
- [A Survey of Deep Learning Methods for Relation Extraction](https://mlsanity.com/paper/1705.03645/) (2017)
- [Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain](https://mlsanity.com/paper/2203.04729/) (2022)
- [A frame semantic overview of NLP-based information extraction for cancer-related EHR notes](https://mlsanity.com/paper/1904.01655/) (2019)
- [Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models](https://mlsanity.com/paper/1802.01255/) (2018)
- [Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition](https://mlsanity.com/paper/1804.05017/) (2018)
- [Biomedical Named Entity Recognition via Dictionary-based Synonym Generalization](https://mlsanity.com/paper/2305.13066/) (2023)

## Abstract (arXiv)

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 Frcorenewslg, 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 Frcorenewslg 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.