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Cited 31 time in webofscience Cited 40 time in scopus
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Comparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA)

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dc.contributor.authorChung, Sehwan-
dc.contributor.authorMoon, Seonghyeon-
dc.contributor.authorKim, Junghoon-
dc.contributor.authorKim, Jungyeon-
dc.contributor.authorLim, Seungmo-
dc.contributor.authorChi, Seokho-
dc.date.accessioned2023-12-13T03:34:51Z-
dc.date.available2023-12-13T03:34:51Z-
dc.date.issued2023-10-
dc.identifier.issn0926-5805-
dc.identifier.issn1872-7891-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/68744-
dc.description.abstractDespite the increasing use of natural language processing (NLP) in the construction domain, no systematic comparison has been conducted between NLP applications in construction and the latest advancements in NLP within the computer science domain. Therefore, this study compares NLP studies in these two domains. Firstly, a bibliometric analysis was performed on 55 publications in state-of-the-art NLP studies, which identified four main research areas in NLP. Secondly, a systematic review of 202 NLP studies in construction was conducted, presenting representative application areas of NLP and their current technical status. The results reveal a decreasing technology gap between NLP in construction and the state-of-the-art. However, the comparison also highlighted gaps in application areas and methodologies, and eight future research opportunities were proposed. This review serves as a foundation for future studies that aim to apply state-of-the-art NLP technologies in the construction domain. © 2023 Elsevier B.V.-
dc.language영어-
dc.language.isoENG-
dc.publisherElsevier BV-
dc.titleComparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA)-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1016/j.autcon.2023.105020-
dc.identifier.scopusid2-s2.0-85165533169-
dc.identifier.wosid001051311500001-
dc.identifier.bibliographicCitationAutomation in Construction, v.154-
dc.citation.titleAutomation in Construction-
dc.citation.volume154-
dc.type.docTypeReview-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaConstruction & Building Technology-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryConstruction & Building Technology-
dc.relation.journalWebOfScienceCategoryEngineering, Civil-
dc.subject.keywordPlusAUTOMATED INFORMATION EXTRACTION-
dc.subject.keywordPlusRISK-MANAGEMENT-
dc.subject.keywordPlusSAFETY-
dc.subject.keywordAuthorBibliometric analysis-
dc.subject.keywordAuthorNatural language processing-
dc.subject.keywordAuthorNLP methods-
dc.subject.keywordAuthorNLP tasks-
dc.subject.keywordAuthorPreferred reporting items for systematic reviews-
dc.subject.keywordAuthorSystematic comparison-
dc.subject.keywordAuthorVOSViewer-
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공과대학 (산업시스템공학부)
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