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NLP and Deep Learning-based Analysis of Building Regulations to Support Automated Rule Checking System
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2018
Year
Artificial IntelligenceDeep Learning-based AnalysisEngineeringMachine LearningAi FoundationSemanticsLanguage ProcessingNatural Language ProcessingData ScienceComputational LinguisticsModel Analysis (Educational Assessment)Language StudiesLanguage ModelsKnowledge RepresentationNlp TaskComputer ScienceSemantic AnalysisDeep LearningSemantic ParsingParsingModel Analysis (Information Engineering)Building RegulationsAutomated ReasoningLinguisticsSemantic Representation
Let's gather each label: Background sentences: - "Automated rule checking has been developed in various ways and enhanced the efficiency of building design review process." - "Converting human-readable building regulations to computer-readable format is, however, still time-consuming and error-prone due to the nature of human languages." - "[Background, Purpose] Several domain-independent efforts have been made for NLP, and this paper focuses on how computers can be able to understand semantic meaning of building regulations to intelligently automate rule interpretation process." So background: context: automated rule checking improves efficiency; converting regulations is time-consuming; domain-independent NLP efforts exist. Purpose sentences: - "[Purpose, Mechanism] NLP and Deep Learning-based Analysis of Building Regulations to Support Automated Rule Checking System ... aims to describe a natural language processing (NLP) and deep learning-based approach for supporting automated rule checking system." - "[Background, Purpose] ... this paper focuses on how computers can be able to understand semantic meaning of building regulations to intelligently automate rule interpretation process." - "[Purpose, Mechanism] This paper proposes a semantic analysis process of regulatory sentences and its utilization for rule checking system." So purpose: to describe and propose a semantic analysis process using NLP and deep learning to support automated rule checking of building regulations. Mechanism sentences: - "[Purpose, Mechanism] ...
NLP and Deep Learning-based Analysis of Building Regulations to Support Automated Rule Checking System Jaeyeol Song, Jinsung Kim and Jin-Kook Lee Pages 586-592 (2018 Proceedings of the 35th ISARC, Berlin, Germany, ISBN 978-3-00-060855-1, ISSN 2413-5844) Abstract: This paper aims to describe a natural language processing (NLP) and deep learning-based approach for supporting automated rule checking system. Automated rule checking has been developed in various ways and enhanced the efficiency of building design review process. Converting human-readable building regulations to computer-readable format is, however, still time-consuming and error-prone due to the nature of human languages. Several domain-independent efforts have been made for NLP, and this paper focuses on how computers can be able to understand semantic meaning of building regulations to intelligently automate rule interpretation process. This paper proposes a semantic analysis process of regulatory sentences and its utilization for rule checking system. The proposed process is composed of following steps: 1) learning semantics of words and sentences, 2) utilization of semantic analysis. For semantic analysis, we use word embedding technique which converts meaning of words in numerical values. By using those values, computers can extract related words and classify the topic of sentences. The results of the semantic analysis can elaborate the interpretation with domain-specific knowledge. This paper also shows a demonstration of the proposed approach. Keywords: Automated rule checking, Natural language processing, Deep learning, Semantic analysis, Building information modeling (BIM) DOI: https://doi.org/10.22260/ISARC2018/0080 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley