Addressing Legal and Contractual Matters in Construction Using Natural Language Processing: A Critical Review

Hassan, Fahad; Le, Tuyen; Lv, Xuan

doi:10.1061/(asce)co.1943-7862.0002122

Cited by 33 publications

(6 citation statements)

References 88 publications

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“…A contract can only be managed properly if the consensus and commonality in the meaning of its provisions are achieved by contracting parties with incompatible interests and complete contract terms (Cheung and Pang, 2013). Linguistic issues along with the biased provisions modified by the clients would make the contract incomplete, threatening parties to understand their obligations (Hassan et al, 2021). All these facts emphasize the need for improvements in contracts to ensure that its provisions, obligations and requirements are comprehensive, clear, complete and unambiguous (Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The role of contract incompleteness factors in project disputes: a hybrid fuzzy multi-criteria decision approach

Gürgün

Koç

2022

ECAM

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PurposeContract incompleteness with deficiency, inconsistency, defectiveness, and ambiguity in contract clauses, which can cause misunderstandings and misinterpretations, may result with disputes in projects. This study aims to investigate contract incompleteness factors with a hybrid fuzzy multi-criteria decision approach.Design/methodology/approachContract incompleteness factors were ranked by fuzzy VIKOR (Visekriterijumska Optimizacija I Kompromisno Resenje) method, and the most significant factors were subjected to fuzzy decision-making trial and evaluation laboratory (DEMATEL) to examine their causal relationships. The study is not limited to ranking the identified factors solely, since their cause-effect interactions are also essential for proper risk management in construction projects.FindingsHybrid use of multi-criteria analysis reveals that ambiguity in enforceability including excessive demands and significant amendments in the scope of works are the top two causal contract incompleteness factors, while lack of implementation details and focus of focal point, and insufficient supporting and technical documents are the most affected ones.Originality/valueContractual causes of disputes due to contract incompleteness factors other than requirements of the contracts have been rarely investigated in the literature. The research is one of the first studies in the literature investigating the causal relationship among factors in construction contracts, which might lead to project disputes. Findings are expected to improve contract drafting, eventually contributing to effective risk management in construction projects.

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Section: Introductionmentioning

confidence: 99%

The role of contract incompleteness factors in project disputes: a hybrid fuzzy multi-criteria decision approach

Gürgün

Koç

2022

ECAM

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“…The validation and evaluation of the method will most likely reveal opportunities for improvements. It is also expected that the forthcoming comparison of the method with knowledge extraction methods recently proposed for the construction engineering domain (Hassan et al 2021;Le, 2020, 2022;Moon et al 2022) will yield optimization options. Several opportunities for improvements that are worth further investigation have already been identified in the present state of the research project.…”

Section: Forthcoming Methods Evaluation and Future Improvementsmentioning

confidence: 99%

“…In recent research studies advanced NLP methods and Machine Learning have been applied to extract specific knowledge items from text documents of the construction domain, such as contractual risk clauses (Moon et al 2022), requirements (Hassan andLe, 2020), and legal and contractual matters (Hassan et al 2021). The results of these studies may offer promising research avenues for the computerbased relevance assessment of environmental regulations targeted in this work.…”

Section: Related Workmentioning

confidence: 99%

Data modeling and NLP-based scoring method to assess the relevance of environmental regulatory announcements

Thimm

2023

Environ Syst Decis

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The constantly growing body of global environmental legislation necessitates that corporate environmental compliance managers frequently assess the relevance of new regulations and regulation revisions for each of their sites. Companies are pressured to streamline and automate this crucial task through digital workflows and specialized IT-based assistance systems. This has recently piqued the interest of researchers working in different disciplines, such as intelligent systems, machine learning, and natural language processing. The article describes the latest results of our long-term research program on IT-based support for corporate compliance management, offering insights for these, and other disciplines. The context and the main aspects of environmental regulation announcements and the relevance assessment task are analyzed. An extensive conceptual data model is developed that serves as a foundation for tailoring a generic method to perform a relevance assessment that considers site-specific individual environmental compliance facts. The method uses heuristic data operations and various text processing techniques from the field of natural language understanding. In order to exemplify the method, two application scenarios are described in which the relevance of new waste management directives are assessed for a multi-site production company.

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“…Topic modeling algorithms like Natural Language Processing (NLP)-based Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) can be employed to meet the objectives of this study. Such NLP-based tools have been used widely in the construction management research domain [ 26 ]. LDA, in particular, is widely used in several domains as a tool for topic modeling [ 27 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of NLP-based topic modeling to analyse unstructured text data in annual reports of construction contracting companies

Jagannathan

Roy

Delhi

2022

CSIT

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The construction industry is the backbone of a nation’s economy. It is a matter of great concern that such an industry suffers from time and cost overruns, especially in these challenging times. Coupled with the overrun issues, the sector is often criticized for lacking adequate quality and quantity of structured secondary data. The emerging technologies in data science and machine intelligence present a unique opportunity to understand the sector better and aid in effective decision-making. To better understand the utility of such technologies, the Management Discussion and Analysis ssections of the annual reports of publicly listed top Indian construction contracting firms are analyzed to identify the presence of ‘strategy themes’ and further map them to the organizations considered. Natural Language Processing (NLP)-based topic modeling algorithms, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), are used in this study to perform a qualitative content analysis to identify the latent themes. From a methodological standpoint, considering the context of this study, the NMF results are better in accuracy, precision, and recall compared with the LDA. The results show that while most construction contracting firms prioritized a ‘revenue-focused’ strategy to expand their order books, a smaller set of large-sized firms seem to prioritize process improvement to improve their execution productivity and therefore are ‘profit margin improvement focused’ or ‘lean-focussed’ in their approach. Although a proof-of-concept, this study unlocks the immense potential of unsupervised NLP-based topic-modeling tools to understand and infer from unstructured and freely available text data in the public domain to aid sectoral analysis and policymaking.

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Addressing Legal and Contractual Matters in Construction Using Natural Language Processing: A Critical Review

Cited by 33 publications

References 88 publications

The role of contract incompleteness factors in project disputes: a hybrid fuzzy multi-criteria decision approach

The role of contract incompleteness factors in project disputes: a hybrid fuzzy multi-criteria decision approach

Data modeling and NLP-based scoring method to assess the relevance of environmental regulatory announcements

Application of NLP-based topic modeling to analyse unstructured text data in annual reports of construction contracting companies

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