Using natural language processing for automated construction contract review during risk assessment at the bidding stage

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2022-12

Author

Eken, Görkem

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Construction sector contains various risks, and construction projects are open to failure due to their nature and the involvement of multiple parties. Contracts are legal documents that are used to define the responsibilities of parties in a project. Risks that are taken by a party are highly related to their positions in the contracts. However, contracts are prepared by clients, and contractors generally do not have enough time to review their responsibilities before preparing their bids. Moreover, contracts may not always be clear in terms of all subjects. As a result, it may lead to ambiguities. Advances in information technology (IT) may provide solutions to the construction sector in this area. Natural Language Processing (NLP) focuses on using computers to understand, process, and manipulate natural language text to achieve a variety of objectives. NLP can be used to review contract documents within seconds depending on the volume of the documents and available processing power. In this study, FIDIC standard forms of contracts were selected and all sentences were labeled with sentence type and ownership in order to create a training dataset for machine learning applications. In addition to the training dataset, the test dataset was created by using a contract of the real construction project. By using created datasets, 5 different machine learning algorithms were trained with different NLP techniques. The results of 12 machine learning models were evaluated, and the selected ones were combined by the ensemble method. In conclusion, sentence types in a FIDIC contract were categorized 89% accurately as heading, definition, obligation, risk, and right. Additionally, related parties for sentences that imply risk right and obligation were predicted 83% accurately. The proposed method can be used by contractors to quickly classify the contract text in order to identify the contractual risks required to decide risk premiums in the tender preparation phase.

Subject Keywords

Construction contract review, Machine learning, Natural language processing, Text classification, Deep learning

URI

https://hdl.handle.net/11511/101793

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

G. Eken, “Using natural language processing for automated construction contract review during risk assessment at the bidding stage,” Ph.D. - Doctoral Program, Middle East Technical University, 2022.