Identifying Contingency Liability from P3 Contracts Using Rule-Based NLP
Publication: Construction Research Congress 2022
ABSTRACT
The COVID-19 pandemic has increased contractual concerns under contingencies for public–private partnership (P3) projects. Conventional manual contract extraction is time-consuming and error-prone. Devising a method for automatic contract extraction can support contract management in this aspect. This research proposes a rule-based natural language processing (NLP) approach to extracting contingency liabilities allocated between the public sector and the private sector in the contract. The model consists of a domain-specific lexicon developed based on 21 US transportation P3 concession agreements and a set of matching rules to identify target sentences which fall into five classes, namely remedy entitlement, remedy obligation, liability waiver, mitigation, and termination. This automatic process can reduce the time and cost of the contract review process and help identify issues that the contracting parties should consider going forward in drafting new contracts or in amending existing contracts to avoid potential disputes, in response to consequences of contingencies, including the COVID-19 pandemic.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Alsharef, A., Banerjee, S., Uddin, S. M., Albert, A., and Jaselskis, E. (2021). “Early impacts of the COVID-19 pandemic on the United States construction industry.” Int. J. Environ. Res. Public Health, 18(4), 1559.
Baxter, D., and Casady, C. B. (2020). “A coronavirus (COVID-19) triage framework for (sub) national public–private partnership (PPP) programs.” Sustainability, 12(13), 5253.
Bilal, M., Oyedele, L., Qadir, J., Munir, K., Ajayi, S., Akinade, O., Owolabi, H., Alaka, H., and Pasha, M. (2016). “Big Data in the construction industry: A review of present status, opportunities, and future trends.” Adv. Eng. Inf., 30(3), 500–521.
Buckland, M., and Gey, F. (1994). “The relationship between recall and precision.” Journal of the American society for information science, 45(1), 12–19.
Casady, C. B., and Baxter, D. (2020). “Pandemics, public-private partnerships (PPPs), and force majeure| COVID-19 expectations and implications.” Construct. Manage. Econ., 38(12), 1077–1085.
FHWA. (2015). Availability Payment Concessions Public-Private Partnerships Model Contract Guide. Federal Highway Administration, Washington, D.C.
Finkel, R. M., Trenor, J. A., and Soiffer, A. (2020). “COVID-19: Drafting force majeure clauses in light of the COVID-19 pandemic.” WilmerHale, <https://www.wilmerhale.com/en/insights/client-alerts/20200413-drafting-force-majeure-clauses-in-light-of-the-covid-19-pandemic>(Jul. 14, 2020).
Hansen, S. (2020). “Does the COVID-19 outbreak constitute a force majeure event? A pandemic impact on construction contracts.” In J. Civ. Eng. Forum, 6(1), 201–214.
Hassan, F., and Le, T. (2020). “Automated requirements identification from construction contract documents using natural language processing”. J. Leg. Aff. Dispute Resolut. Eng. Constr., 12(2), 04520009.
Hovy, P. (2015). “Risk allocation in public-private partnerships: Maximizing value for money.” International Institute for Sustainable Development, <https://www.iisd.org/publications/risk-allocation-public-private-partnerships-maximizing-value-money>(Jul. 14, 2020).
Katz, J. (2020). “COVID-19 supply chain impacts & construction delays.” VERTEX, <https://vertexeng.com/insights/covid-19-supply-chain-impacts-construction-delays/>(Jul. 25, 2020).
Lee, J., Yi, J., and Son, J. (2019). “Development of automatic-extraction model of poisonous clauses in international construction contracts using rule-based NLP.” J. Comput. Civ. Eng., 33(3), 04019003.
Lee, J., Ham, Y., Yi, J., and Son, J. (2020). “Effective risk positioning through automated identification of missing contract conditions from the contractor’s perspective based on FIDIC contract cases.” J. Manage. Eng., 36(3), 05020003.
Li, Y., Guzman, E., Tsiamoura, K., Schneider, F., and Bruegge, B. (2015). “Automated requirements extraction for scientific software.” Procedia Comput. Sci., 51, 582–591.
Yan, H., Yang, N., Peng, Y., and Ren, Y. (2020). “Data mining in the construction industry: Present status, opportunities, and future trends.” Autom. Constr., 119, 103331.
Pan, Y., and Zhang, L. (2021). “Roles of artificial intelligence in construction engineering and management: A critical review and future trends.” Autom. Constr., 122, 103517.
Shaughnessy, W. J., Underwood, W. E., and Cazenave, C. (2020). “COVID-19’s impact on construction: Is there a remedy? — Time extension, force majeure, or more?.” The National Law Review, <https://www.natlawreview.com/article/covid-19-s-impact-construction-there-remedy-time-extension-force-majeure-or-more> (Jul. 15, 2020).
Slankas, J., and Williams, L. (2013). “Automated extraction of non-functional requirements in available documentation.” 2013 1st International workshop on natural language analysis in software engineering (NaturaLiSE), IEEE, 9–16.
Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., and Bowman, D. (2016). “Automated content analysis for construction safety: A natural language processing system to extract precursors and outcomes from unstructured injury reports.” Autom. Constr., 62, 45–56.
Wimalasuriya, D. C., and Dou, D. (2010). “Ontology-based information extraction: An introduction and a survey of current approaches.” J. Inf. Sci., 36(3), 306–323.
Zhang, J., and El-Gohary, N. M. (2016). “Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking.” J. Comput. Civ. Eng., 30(2), 04015014.
Information & Authors
Information
Published In
Construction Research Congress 2022
Pages: 59 - 68
History
Published online: Mar 7, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.