최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국건설관리학회논문집 = Korean journal of construction engineering and management, v.21 no.6, 2020년, pp.125 - 134
정상원 (한국산업융합연구원) , 정기창 (한국산업융합연구원)
Natural language encountered in construction documents largely deviates from those that are recommended by the authorities. Such practice that is lacking in coherence will discourage integrated research with automation, and it will hurt the productivity in the industry for the long run. This researc...
Abiteboul, S. (1997). Querying semi-structured data. InInternational Conference on Database Theory, Springer, Berlin, Heidelberg, pp. 1-18.
An, M.G., Kim, M.J., and Kim, Y.S. (2019). "A BlockchainBased Risk Management for Overseas Construction Project Using Natural Language Processing." Proceedings of KICEM University Students Conference, pp. 116-119.
Bilal, M., Oyedele, L.O., Qadir, J., Munir, K., Ajayi, S.O., Akinade, O.O., and Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities and future trends. Advanced engineering informatics, 30(3), pp. 500-521.
Cambria, E., and White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), pp. 48-57.
Han, K.K., and Golparvar-Fard, M. (2017). Poten-tial of big visual data and building informationmodeling for construction performance analyt-ics: An exploratory study. Automation in Con-struction, 73, pp. 184-198.
Kim, Y.R., Lee, S.H., and Park, S.H. (2012). "Development of Rule-Set Definition for Architectural Design Code Checking based on BIM." Korean Journal of Construction Engineering and Management, KICEM, 13(6) pp. 143-152.
Lee, J.H. (2019). "Review on Natural Language Processing Reasearch Utilizing Unstructured Text Data in Construction Industry." Construction Engineering and Management, 20(2), pp. 62-66.
Lalwani, M., Bagmar, N., and Parikh, S. (2014). "Efficient Algorithm for Auto Correction Using ngram Indexing." International Journal of Computer & Communication Technology (IJCCT), 3(3), pp. 23-27.
McGuinness, D. L. (2004). OWL web ontology language overview. W3C recommendation. http://www.w3.org/TR/owl-features.
Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), 3-26.
Park, Y.S., Oh, C.D., Jeon, Y.S., and Park, C.S. (2008). "A Web-Based Construction Failure Information System using Case-Based Reasoning." Korean Journal of Construction Engineering and Management, KICEM, 9(6), pp. 257-267.
Stone, M. (1974). Crossvalidatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B (Methodological), 36(2), pp. 111-133.
Williams, T.P., and Gong, J. (2014). "Predicting construction cost overrunsusing text mining, numerical data and ensemble classifiers." Automation in Construction, 43, pp. 23-29.
Zhang, J., and El-Gohary, N.M. (2016). "Semantic nlp-based information extrac-tion from construction regulatory documents for automated compliancechecking." Journal of Computing in Civil Engineering, 30(2), 04015014.
Y.S. Kim. (2019). Automatic multi-label image classification model for construction site images (Unpublished doctoral dissertation). Graduate School, Seoul National University.
Eunjeong L. Park, Sungzoon Cho. (2014). KoNLPy: Korean natural language processing in Python. 26th Annual Conference on Human and Language Technology, pp. 1-4.
KAIST CILab (2014). Hannanum Morphological Analysis http://semanticweb.kaist.ac.kr/hannanum/index.html
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
오픈액세스 학술지에 출판된 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.