Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT)

Moon, Seonghyeon; Chi, Seokho; Im, Seok-Been

doi:10.1016/j.autcon.2022.104465

Cited by 41 publications

(15 citation statements)

References 56 publications

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“…We list the following models as benchmark models for the TSR task: TabbyPDF [19], GraphTSR [7], DeepDeSRT [8], and CATT-Net [17]. We implement four models by finetuning the Glove [20], FastText [21], and BERT [15] in accordance with the well-known pre-trained fine-tune paradigm in order to compare the state-of-the-art NLP techniques. This is because our proposed multi-task model depends on features from both image modal and text modal.…”

Section: A Implementation Details and Experimental Resultsmentioning

confidence: 99%

“…A huge language model for a broadly organised table called TUTA [3] was developed using a large corpus of spreadsheets. In addition, other language models, such BERT [15], can also be used to the CTC issue. These research all concentrate on spreadsheets because they provide intrinsic structural data that may be used to train and improve contextbased language models.…”

Section: B Table Cell Type Classificationmentioning

confidence: 99%

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Handling Big Tabular Data of Ict Supply Chains: A Multi-Task, Machine-Interpretable Approach

Kohale,

Sharma

2023

AJCT

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The essential details of ICT devices are frequently distilled into large tabular data sets that are distributed throughout supply chains as a result of the features of Information and Communications Technology (ICT) goods. With the explosion of electronic assets, it is crucial to automatically analyse tabular structures. We develop a Table Structure Recognition (TSR) work and a Table Cell Type Classification (CTC) task to convert the tabular data in electronic documents into a machine-interpretable format and give layout and semantic information for information extraction and interpretation. For the TSR job, complicated table structures are represented using a graph. Table cells are divided into three groups-Header, Attribute, and Data-based on how they work for the CTC job. Then, utilising the text modal and picture modal characteristics, we provide a multi-task model to accomplish the two tasks concurrently. Our test findings demonstrate that, using the ICDAR2013 and UNLV datasets, our suggested strategy can beat cuttingedge approaches.

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Section: A Implementation Details and Experimental Resultsmentioning

confidence: 99%

Section: B Table Cell Type Classificationmentioning

confidence: 99%

Handling Big Tabular Data of Ict Supply Chains: A Multi-Task, Machine-Interpretable Approach

Kohale,

Sharma

2023

AJCT

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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%

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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“…The tendering process is a crucial phase in the construction project for companies. The quality of bids can determine whether companies are awarded construction contracts, as well as their loss or profit from the project (Aman and Azeanita, 2021; Moon et al , 2022). Additional costs can lead to severe problems for all stakeholders, including possible work stoppages due to the lack of resources (Alaka et al , 2019; Pessoa et al , 2021; Suneja et al , 2021).…”

Section: Introductionmentioning

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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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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Automated detection of contractual risk clauses from construction specifications using bidirectional encoder representations from transformers (BERT)

Cited by 41 publications

References 56 publications

Handling Big Tabular Data of Ict Supply Chains: A Multi-Task, Machine-Interpretable Approach

Handling Big Tabular Data of Ict Supply Chains: A Multi-Task, Machine-Interpretable Approach

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

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

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