건설 산업 내 비정형 텍스트 데이터를 활용한 자연어 처리 (Natural Language Processing, NLP) 기반의 글로벌 연구 동향

  • Published : 2019.04.01

Abstract

Keywords

References

  1. Caldas, C. H., Soibelman, L., and Han, J. (2002). Automated classification of construction project documents. Journal of Computing in Civil Engineering, 16(4), 234-243. https://doi.org/10.1061/(ASCE)0887-3801(2002)16:4(234)
  2. Caldas, C. H., and Soibelman, L. (2003). Automating hierarchical document classification for construction management information systems. Automation in Construction, 12(4), 395-406. https://doi.org/10.1016/S0926-5805(03)00004-9
  3. Chopra, D., Joshi, N., and Mathur, I. (2016). Mastering Natural Language Processing with Python. Packt Publishing Ltd., Birmingham, UK.
  4. Fan, H., and Li, H. (2013). Retrieving similar cases for alternative dispute resolution in construction accidents using text mining techniques. Automation in construction, 34, 85-91. https://doi.org/10.1016/j.autcon.2012.10.014
  5. Gao, G., Liu, Y. S., Wang, M., Gu, M., and Yong, J. H. (2015). A query expansion method for retrieving online BIM resources based on Industry Foundation Classes. Automation in construction, 56, 14-25. https://doi.org/10.1016/j.autcon.2015.04.006
  6. Kao, A., and Poteet, S. R. (Eds.). (2007). Natural language processing and text mining. Springer Science & Business Media.
  7. Liu, K., and El-Gohary, N. (2017). Ontology-based semi-supervised conditional random fields for automated information extraction from bridge inspection reports. Automation in Construction. 81, 313-327. https://doi.org/10.1016/j.autcon.2017.02.003
  8. Manning, C. D., Raghavan P. and Schutze, H. (2008). Introduction to information retrieval. Cambridge University Press, New York, USA.
  9. Mohemad, R., Hamdan, A. R., Othman, Z. A., and Noor, N. M. M. (2011). Ontological-based information extraction of construction tender documents. Proceedings of Advances in Intelligent Web Mastering-3 Springer, Berlin, Heidelberg, 153-162.
  10. Salama, D. M., and El-Gohary, N. M. (2016). Semantic Text Classification for Supporting Automated Compliance Checking in Construction. Journal of Computing in Civil Engineering, 30(1). doi:10.1061/(ASCE)CP.1943-5487.0000301.
  11. Tiwary, U. S., and Siddiqui, T. (2008). Natural language processing and information retrieval. Oxford University Press, Inc.. New Delhi, India.
  12. Williams, T. P., and Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43, 23-29. https://doi.org/10.1016/j.autcon.2014.02.014
  13. Zhang, J., and El-Gohary, N. M. (2013). Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking. Journal of Computing in Civil Engineering, 30(2), 04015014.
  14. Zhang, J., and El-Gohary, N. M. (2015). Automated information transformation for automated regulatory compliance checking in construction. Journal of Computing in Civil Engineering, 29(4), B4015001.
  15. Zou, Y., Kiviniemi, A., and Jones, S. W. (2017). Retrieving similar cases for construction project risk management using natural language processing techniques. Automation in Construction, 80, 66-76. https://doi.org/10.1016/j.autcon.2017.04.003