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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)

Authors
Chung, SehwanMoon, SeonghyeonKim, JunghoonKim, JungyeonLim, SeungmoChi, Seokho
Issue Date
Oct-2023
Publisher
Elsevier BV
Keywords
Bibliometric analysis; Natural language processing; NLP methods; NLP tasks; Preferred reporting items for systematic reviews; Systematic comparison; VOSViewer
Citation
Automation in Construction, v.154
Indexed
SCIE
SCOPUS
Journal Title
Automation in Construction
Volume
154
URI
https://scholarworks.gnu.ac.kr/handle/sw.gnu/68744
DOI
10.1016/j.autcon.2023.105020
ISSN
0926-5805
1872-7891
Abstract
Despite 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.
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공과대학 > Department of Industrial and Systems Engineering > Journal Articles

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공과대학 (산업시스템공학부)
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