Multi-Class Categorization of Design-Build Contract Requirements Using Text Mining and Natural Language Processing Techniques

Hassan, Fahad; Le, Tuyen; Tran, Duc-Hoc

doi:10.1061/9780784482889.135

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The data set consists of text and words that ML algorithms cannot directly identify. These terms are converted into feature vectors; Algorithm selection and training : Code different NLP algorithms and train them with a large part of the pre-prepared data set; Model testing : Test the different NLP algorithms with the remaining part of the data set; and Model evaluation : Evaluation of the performance of the algorithms in terms of accuracy and errors via metrics (Ul Hassan et al , 2020; Akanbi and Zhang, 2021). …”

Section: Resultsmentioning

confidence: 99%

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Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Jacques de Sousa,

Poças Martins,

Sanhudo

et al. 2024

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Purpose This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding. Design/methodology/approach This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction. Findings This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools. Originality/value Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

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Section: Resultsmentioning

confidence: 99%

“…Model evaluation : Evaluation of the performance of the algorithms in terms of accuracy and errors via metrics (Ul Hassan et al , 2020; Akanbi and Zhang, 2021).…”

Section: Resultsmentioning

confidence: 99%

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Jacques de Sousa,

Poças Martins,

Sanhudo

et al. 2024

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“…In Lee et al 6 , the authors proposed an automated model for extracting contract-risk to detect "poisonous" clauses in contracts, aimed at supporting contract management for construction companies. Other works [7][8][9][10][11][12] , similarly focused on the extraction or identification of specific types of contractual statements or clauses, such as obligatory or non-obligatory, ambiguous, or non-ambiguous clauses, contractual risk clauses, and specific contract elements. To the best of our knowledge, our work is the first to concentrate on a fine-grained classification of contractual obligation statements into 152 distinct classes to facilitate contracts governance.…”

Section: Related Workmentioning

confidence: 99%

A data decomposition-based hierarchical classification method for multi-label classification of contractual obligations for the purpose of their governance

Singh,

Rose Anish,

Verma

et al. 2024

Sci Rep

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Contract governance ensures that the agreed outcomes between customers and vendors are fulfilled. Information Technology (IT) outsourcing organizations enter thousands of contractual relationships each month leading to a high volume of business-critical contractual text that must be reviewed and deciphered for effective governance. The key to effective governance of contracts is a model that facilitates assigning ownership of the obligations to the right departments in an organization and allocating their accountability to the right stakeholders. For this, the contractual obligations need to be identified and classified so that details such as actions to be taken by departments in an organization and their ownership as per a given clause are brought out for the purpose of governance. In this paper, we present our work on automated extraction and classification of obligations present in Software Engineering (SE) contracts for the purpose of contracts governance. We propose a novel data decomposition-based hierarchical classification method for a multi-label classification of contractual obligations. We conducted experiments for a fine-grained automated classification of more than 55,000 statements from 50 large real-life SE contract documents received from a large vendor organization into 152 governance-specific classes. The results indicate that the proposed method can bring about a 7–8% improvement in accuracies when compared to state-of-the-art classification baselines such as BERT, RoBERTa, and generative models such as GPT-2.

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“…No presente artigo, focou -se a segunda solução, sendo apresentada, na Figura 2, um framework para o desenvolvimento de uma solução deste tipo. Como visível nesta figura, podemos generalizar a framework para o desenvolvimento de um modelo de classificação de texto com recurso a NLP em seis etapas [22]:…”

Section: Abordagem Proposta Para O Desenvolvimento Da Ferramenta Idea...unclassified

Algoritmos de classificação de texto na automatização dos processos orçamentação

Sousa

Martins

Baptista

et al. 2022

4º Congresso Português De ‘Building Information Modelling’ Vol. 2 - ptBIM

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Multi-Class Categorization of Design-Build Contract Requirements Using Text Mining and Natural Language Processing Techniques

Cited by 6 publications

References 15 publications

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

Automation of text document classification in the budgeting phase of the Construction process: a Systematic Literature Review

A data decomposition-based hierarchical classification method for multi-label classification of contractual obligations for the purpose of their governance

Algoritmos de classificação de texto na automatização dos processos orçamentação

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