Natural Language Processing–Driven Model to Extract Contract Change Reasons and Altered Work Items for Advanced Retrieval of Change Orders
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 11
Abstract
Change orders are documents that describe a specific contract amendment to the original scope of work. Historical change orders are invaluable information sources that can provide practical and proven solutions for developing new change orders from similar cases. However, current change order management systems are not efficient in searching for and finding the most related and similar change orders due to inherent weaknesses in current archiving and search processes, such as keyword-based or reason code–based search. This study proposes and develops a natural language processing (NLP)–driven model that can significantly improve the accuracy and reliability of searching cases by restructuring how each change order’s information is stored and retrieved in change order management systems. The NLP-driven model proposed in this study can automatically detect change reasons and altered work items through text representation pattern analysis and training. The proposed model applies semantic frames to define essential semantic components and determines syntactic features for text representation pattern analysis. The model also utilizes a conditional random field (CRF) classifier, which can consider contexts in sequential texts at the model training stage. The proposed model can significantly improve the accuracy and relevancy of the search process to find the most similar cases by allowing context-driven classification, archiving, and retrieval of change orders.
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Data Availability Statement
The data used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the acknowledgments. Some models or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to acknowledge that the Oklahoma DOT provided the historical change order data for this study. This work was financially supported by an Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korean Government (MSIT) (No. 2019-0-01559-001), Digitalizing Construction Project Requirements Using Artificial Intelligence and Natural Language Processing.
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Journal of Construction Engineering and Management
Volume 147 • Issue 11 • November 2021
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© 2021 American Society of Civil Engineers.
History
Received: Dec 16, 2020
Accepted: Jun 24, 2021
Published online: Aug 27, 2021
Published in print: Nov 1, 2021
Discussion open until: Jan 27, 2022
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