State-of-the-Art Review on the Applicability of Natural Language Processing (NLP) Methods to Address Legal Issues in Construction
Publication: Construction Research Congress 2022
ABSTRACT
Claims and legal disputes in construction projects largely result in cost overruns, delays, and adversarial working relationships among contracting parties. Most disputes in construction projects are caused by the inadequate drafting, review, and management of legal documents such as contracts, standards, and codes. In this regard, Natural language processing (NLP) offers a collection of methods that can be employed to process the complex text of legal documents to prevent the root causes of disputes. Although several researchers have investigated the role of NLP in addressing legal issues, this line of research is still at a very early stage. The current study presents a comprehensive review of available literature on the exploration of NLP in preventing several common legal issues associated with drafting, reviewing, and managing different legal documents. Identifying and documenting previously developed NLP-based frameworks, their effectiveness, and limitations could help researchers get critical information to further explore NLP in minimizing legal issues.
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REFERENCES
Al Qady, M., and Kandil, A. (2010). Concept Relation Extraction from Construction Documents Using Natural Language Processing. Journal of Construction Engineering and Management, 136(3), 294–302. https://doi.org/10.1061/(asce)co.1943-7862.0000131.
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.
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).
Çevikol, S., and Aydemir, F. B. (2019). Detecting inconsistencies of natural language requirements in satellite ground segment domain. CEUR Workshop Proceedings, 2376.
Chakrabarti, D., Patodia, N., Bhattacharya, U., Mitra, I., Roy, S., Mandi, J., Roy, N., and Nandy, P. (2019). Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2018-Octob(October), 683–688. https://doi.org/10.1109/TENCON.2018.8650382.
Chan, E. H., and Suen, H. C. (2005). Disputes and dispute resolution systems in Sino-foreign joint venture construction projects in China. Jounral of Professional Issues in Engineering Education and Practice, 131(2), 141–148. https://doi.org/10.1061/(ASCE)1052-3928(2005)131.
Curtotti, M., and McCreath, E. C. (2011). A corpus of Australian contract language: Description, profiling and analysis. Proceedings of the International Conference on Artificial Intelligence and Law, 199–208. https://doi.org/10.1145/2018358.2018387.
Demasco, P. W., and McCoy, K. F. (1992). Generating Text from Compressed Input: An Intelligent Interface for People with Severe Motor Impairments. Communications of the ACM, 35(5), 68–78. https://doi.org/10.1145/129875.129881.
Hassan, F., Le, T., and Tran, D. H. (2020). Multi-Class Categorization of Design-Build Contract Requirements Using Text Mining and Natural Language Processing Techniques. Construction Research Congress 2020: Project Management and Controls, Materials, and Contracts, 1266–1274.
Hassan, F. U., and Le, T. (2020a). Automated requirements identification from construction contract documents using natural language processing. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 12((in press)), 1–12. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000379.
Hassan, F. U., and Le, T. (2020b). Ontology-Based Decoding of Risks Encoded in the Prescriptive Requirements in Bridge Design Codes. Proceedings of the 37th International Symposium on Automation and Robotics in Construction (ISARC), Isarc, 98–105. https://doi.org/10.22260/isarc2020/0016.
Hassan, F. U., and Le, T. (2021). Computer-assisted separation of design-build contract requirements to support subcontract drafting. Automation in Construction, 122(October 2020), 103479. https://doi.org/10.1016/j.autcon.2020.103479.
Iyer, K. C., Chaphalkar, N. B., and Joshi, G. A. (2008). Understanding time delay disputes in construction contracts. International Journal of Project Management, 26(2), 174–184. https://doi.org/10.1016/j.ijproman.2007.05.002.
Jagannathan, M., and Delhi, V. S. K. (2019). Litigation Proneness of Dispute Resolution Clauses in Construction Contracts. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 11(3), 4519011.
Lee, J., Ham, Y., Yi, J. S., and Son, J. (2020). Effective Risk Positioning through Automated Identification of Missing Contract Conditions from the Contractor’s Perspective Based on FIDIC Contract Cases. Journal of Management in Engineering, 36(3). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000757.
Lee, J., Yi, J.-S. S., and Son, J. (2019). Development of Automatic-Extraction Model of Poisonous Clauses in International Construction Contracts Using Rule-Based NLP. Journal of Computing in Civil Engineering, 33(3), 04019003. https://doi.org/10.1061/(asce)cp.1943-5487.0000807.
Manning, C. D., and Schütze, H. (1999). Foundations of statistical natural language processing.
Salama, D. M., and El-Gohary, N. M. (2013). Semantic Text Classification for Supporting Automated Compliance Checking in Construction. Journal of Computing in Civil Engineering, 30(1), 04014106. https://doi.org/10.1061/(asce)cp.1943-5487.0000301.
Serag, E., Osman, H., and Ghanem, M. (2010). Semantic Detection of Risks and Conflicts in Construction Contracts. Proceedings of the CIB W78 2010: 27th International Conference, 16–18.
Song, J., Kim, J., and Lee, J.-K. (2018). NLP and Deep Learning-based Analysis of Building Regulations to Support Automated Rule Checking System. ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 1–7.
Walsh, K. P. (2017). Identifying and Mitigating the Risks Created by Problematic Clauses in Construction Contracts. Journal of Legal Affairs and Dispute Resolution in Engineering and Construction, 9(3), 03717001. https://doi.org/10.1061/(asce)la.1943-4170.0000225.
Wang, T., Chen, P., Amaral, K., and Qiang, J. (2016). An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification.
Xu, X., and Cai, H. (2019). Semantic Frame-Based Information Extraction from Utility Regulatory Documents to Support Compliance Checking. In Advances in Informatics and Computing in Civil and Construction Engineering (pp. 223–230). Springer International Publishing. https://doi.org/10.1007/978-3-030-00220-6_27.
Younis, G., Wood, G., and Malak, M. A. A. (2008). Minimizing construction disputes: the relationship between risk allocation and behavioural attitudes. Proceedings of CIB International Conference on Building Education & Research BEAR2008, Sri Lanka, 134–135. https://www.irbnet.de/daten/iconda/CIB11538.pdf.
Zait, F., and Zarour, N. (2019). Addressing Lexical and Semantic Ambiguity in Natural Language Requirements. 5th International Symposium on Innovation in Information and Communication Technology, ISIICT 2018. https://doi.org/10.1109/ISIICT.2018.8613726.
Zhang, J., and El-Gohary, N. (2012). Extraction of construction regulatory requirements from textual documents using natural language processing techniques. Computing in Civil Engineering, 453–460.
Zhang, J., and El-Gohary, N. M. (2016a). Extending Building Information Models Semiautomatically Using Semantic Natural Language Processing Techniques. Journal of Computing in Civil Engineering, C4016004.
Zhang, J., and El-Gohary, N. M. (2016b). Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking. Journal of Computing in Civil Engineering, 30(2), 04015014. https://doi.org/10.1061/(asce)cp.1943-5487.0000346.
Zhang, J. (2011). Automated information extraction from construction-related regulatory documents for automated compliance checking.
Zhang, J., and El-Gohary, N. M. (2017). Integrating semantic NLP and logic reasoning into a unified system for fully-automated code checking. Automation in Construction, 73, 45–57. https://doi.org/10.1016/j.autcon.2016.08.027.
Zhou, P., and El-Gohary, N. (2016). Domain-Specific Hierarchical Text Classification for Supporting Automated Environmental Compliance Checking. Journal of Computing in Civil Engineering, 30(4), 04015057. https://doi.org/10.1061/(asce)cp.1943-5487.0000513.
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Published online: Mar 7, 2022
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