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Article: Natural language processing for smart construction: Current status and future directions

TitleNatural language processing for smart construction: Current status and future directions
Authors
KeywordsArtificial intelligence
Construction 4.0
Construction management
Data mining
Natural language processing
Project management
Review
Smart construction
Text mining
Issue Date2022
Citation
Automation in Construction, 2022, v. 134, article no. 104059 How to Cite?
AbstractUnstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements, this paper presents a comprehensive review of bottom-level techniques and mainstream applications of NLP in the industry. In total, 124 journal articles published in the last two decades are reviewed. NLP involves five core steps supported by various techniques, e.g., syntactic parsing, heuristic rules, machine learning, and deep learning. NLP has been applied for information extraction and exchanging and many downstream applications to facilitate management and decision-making. The role of NLP in smart construction and current challenges for fully reaping its benefits are discussed, and four research directions are identified, i.e., improving relation extraction, realising knowledge base auto-development, integrating multi-modal information, and achieving an accuracy-efficiency trade-off by developing an NLP application framework. It is envisioned that outcomes of this paper can assist both researchers and industrial practitioners with appreciating the research and practice frontier of NLP for smart construction and soliciting the latest NLP techniques.
Persistent Identifierhttp://hdl.handle.net/10722/326330
ISSN
2023 Impact Factor: 9.6
2023 SCImago Journal Rankings: 2.626
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWu, Chengke-
dc.contributor.authorLi, Xiao-
dc.contributor.authorGuo, Yuanjun-
dc.contributor.authorWang, Jun-
dc.contributor.authorRen, Zengle-
dc.contributor.authorWang, Meng-
dc.contributor.authorYang, Zhile-
dc.date.accessioned2023-03-09T09:59:50Z-
dc.date.available2023-03-09T09:59:50Z-
dc.date.issued2022-
dc.identifier.citationAutomation in Construction, 2022, v. 134, article no. 104059-
dc.identifier.issn0926-5805-
dc.identifier.urihttp://hdl.handle.net/10722/326330-
dc.description.abstractUnstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements, this paper presents a comprehensive review of bottom-level techniques and mainstream applications of NLP in the industry. In total, 124 journal articles published in the last two decades are reviewed. NLP involves five core steps supported by various techniques, e.g., syntactic parsing, heuristic rules, machine learning, and deep learning. NLP has been applied for information extraction and exchanging and many downstream applications to facilitate management and decision-making. The role of NLP in smart construction and current challenges for fully reaping its benefits are discussed, and four research directions are identified, i.e., improving relation extraction, realising knowledge base auto-development, integrating multi-modal information, and achieving an accuracy-efficiency trade-off by developing an NLP application framework. It is envisioned that outcomes of this paper can assist both researchers and industrial practitioners with appreciating the research and practice frontier of NLP for smart construction and soliciting the latest NLP techniques.-
dc.languageeng-
dc.relation.ispartofAutomation in Construction-
dc.subjectArtificial intelligence-
dc.subjectConstruction 4.0-
dc.subjectConstruction management-
dc.subjectData mining-
dc.subjectNatural language processing-
dc.subjectProject management-
dc.subjectReview-
dc.subjectSmart construction-
dc.subjectText mining-
dc.titleNatural language processing for smart construction: Current status and future directions-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.autcon.2021.104059-
dc.identifier.scopuseid_2-s2.0-85125895378-
dc.identifier.volume134-
dc.identifier.spagearticle no. 104059-
dc.identifier.epagearticle no. 104059-
dc.identifier.isiWOS:000741695500001-

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