Acknowledgement
Supported by : 국토교통부, 한국연구재단
References
- Arcadis (2015). Global Construction Disputes Report 2015, 6-31.
- Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. https://doi.org/10.1145/2133806.2133826
- Kangari, R., & Riggs, L. S. (1989). Construction risk assessment by linguistics. Engineering Management, IEEE Transactions on, 36(2), 126-131. doi: 10.1109/17.18829.
- Lee, J., Son, J., & Yi, J. (2014). The application of text mining techniques for analysis of overseas construction dispute cases, Proceedings of Korea Institute of Construction Engineering and Management, 2014-11, 83-84.
- Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to information Retrieval, vol. 1, Cambridge university press Cambridge, 116-121.
- Meyer, D., Hornik, K., & Feinerer, I. (2008) Text Mining Infrastructure in R. Journal of Statistical Software, 25 (5). pp. 1-54. ISSN 1548-7660
- Fan, H., & 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
- Williams, T. P., & 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
- Yim, D. (2015), Big data analysis using R, Free academy, 21-50.