Part-of-speech tagging of building codes empowered by deep learning and transformational rules

Abstract

Automated building code compliance checking systems were under development for many years. However, the excessive amount of human inputs needed to convert building codes from natural language to computer understandable formats severely limited their range of applicable code requirements. To address that, automated code compliance checking systems need to enable an automated regulatory rule conversion. Accurate Part-of-Speech (POS) tagging of building code texts is crucial to this conversion. Previous experiments showed that the state-of-the-art generic POS taggers do not perform well on building codes. In view of that, the authors are proposing a new POS tagger tailored to building codes. It utilizes deep learning neural network model and error-driven transformational rules. The neural network model contains a pre-trained model and one or more trainable neural layers. The pre-trained model was fine-tuned on Part-of-Speech Tagged Building Codes (PTBC), a POS tagged building codes dataset. The fine-tuning of pre-trained model allows the proposed POS tagger to reach high precision with a small amount of available training data. Error-driven transformational rules were used to boost performance further by fixing errors made by the neural network model in the tagged building code. Through experimental testing, the authors found a well-performing POS tagger for building codes that had one bi-directional LSTM trainable layer, utilized BERT_Cased_Base pre-trained model and was trained 50 epochs. This model reached a 91.89% precision without error-driven transformational rules and a 95.11% precision with error-driven transformational rules, which outperformed the 89.82% precision achieved by the state-of-the-art POS taggers.