A building regulation question answering system: A deep learning methodology

摘要

Regulations play an important role in assuring the quality of a building's construction and minimizing its adverse environmental impacts. Engineers and the like need to retrieve regulatory information to ensure a building conforms to specified standards. Despite the availability of search engines and digital databases that can be used to store regulations, engineers, for example, are unable to retrieve information for domain-specific needs in a timely manner. As a consequence, users often have to deal with the burden of browsing and filtering information, which can be a time-consuming process. This research develops a robust end-to-end methodology to improve the efficiency and effectiveness of retrieving queries pertaining to building regulations. The developed methodology integrates information retrieval with a deep learning model of Natural Language Processing (NLP) to provide precise and rapid answers to user's questions from a collection of building regulations. The methodology is evaluated and a prototype system to retrieve queries is developed. The paper's contribution is therefore twofold as it develops a: (1) methodology that combines NLP and deep learning to be able to address queries raised about the building regulations; and (2) chatbot of question answering system, which we refer to as QAS4CQAR. Our proposed methodology has powerful feature representation and learning capability and therefore can potentially be adopted to building regulations in other jurisdictions.