Construction Research Congress 2020
Syntactic Approach to Extracting Key Elements of Work Modification Cause in Change-Order Documents
Publication: Construction Research Congress 2020: Project Management and Controls, Materials, and Contracts
ABSTRACT
Work modification during the construction phase is quite common, and it typically creates a detrimental impact on project performance significantly. A change-order is developed by affected parties followed by legal agreements that amend to the original contract. Information related to the causes of the modification and its effects, in particular, can provide project participants with useful knowledge that may prevent them from encountering similar changes in the next projects. However, the identification and use of the causal information of a modification is challenging because most information in a change-order is available in a text data format, which is typically unstructured. Thus, the unstructured format makes it difficult to process considerable amounts of information systematically. It also requires a substantial amount of manual effort due to different types of written expressions of the same context or meaning, and dependency on specialized and technical knowledge and jargons. This study presents a process model that automatically extracts key elements related to work modification causes in change-orders using the shortest dependency path (SDP) and conditional random field (CRF) model. The research process is comprised of four tasks: a) build a raw data repository from historical change-orders, b) frame semantic-based key elements labeling, c) create SDP between key nodes, and d) apply conditional random field (CRF) model for label classification. Change-orders obtained from real highway projects have been used to evaluate and verify the model. This research contributes to building a methodological and practical foundation for extracting dependency-based information from construction documents. Project participants can use this method to analyze past projects and assess change-order related risks for new projects before construction begins.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Alnuaimi, A. S., Taha, R. A., Mohsin, M. A., and Al-Harthi, A. S. (2010). "Causes, Effects, Benefits, and Remedies of Change Orders on Public Construction Projects in Oman." Journal of Construction Engineering and Management, 136(5), 615-622.
Ambati, V. (2008). "Dependency structure trees in syntax based machine translation." Proc., Adv. MT Seminar Course Report.
Bunescu, R. C., and Mooney, R. J. (2005). "A shortest path dependency kernel for relation extraction." Proc., Proceedings of the conference on human language technology and empirical methods in natural language processing, 724-731.
Cuong, N. V., Ye, N., Lee, W. S., and Chieu, H. L. (2014). "Conditional random field with high-order dependencies for sequence labeling and segmentation." The Journal of Machine Learning Research, 15(1), 981-1009.
Fillmore, C. J., and Atkins, B. T. (1992). "Toward a frame-based lexicon: The semantics of RISK and its neighbors." Frames, fields, and contrasts: New essays in semantic and lexical organization, 103, 75-102.
Fillmore, C. J., and Baker, C. F. (2001). "Frame semantics for text understanding." Proc., Proceedings of WordNet and Other Lexical Resources Workshop, NAACL.
Fundel, K., Küffner, R., and Zimmer, R. (2006). "RelEx—Relation extraction using dependency parse trees." Bioinformatics, 23(3), 365-371.
Hua, L., and Quan, C. (2016). "A shortest dependency path based convolutional neural network for protein-protein relation extraction." BioMed research international, 2016.
Jiang, J., and Zhai, C. (2007). "A systematic exploration of the feature space for relation extraction." Proc., Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference, 113-120.
Kaijian, L., and El-Gohary, N. (2017). "Similarity-Based Dependency Parsing for Extracting Dependency Relations from Bridge Inspection Reports." Computing in Civil Engineering 2017, 316-323.
Kim, T., and Chi, S. (2019). "Accident Case Retrieval and Analyses: Using Natural Language Processing in the Construction Industry." Journal of Construction Engineering and Management, 145(3).
Kübler, S., McDonald, R., and Nivre, J. (2009). "Dependency parsing." Synthesis Lectures on Human Language Technologies, 1(1), 1-127.
Le, T., Le, C., Jeong, H. D., Gilbert, S. B., and Chukharev-Hudilainen, E. (2019). "Requirement text detection from contract packages to support project definition determination." Advances in Informatics and Computing in Civil and Construction Engineering, 569-576.
Lee, J., and Yi, J.-S. (2017). "Predicting Project’s Uncertainty Risk in the Bidding Process by Integrating Unstructured Text Data and Structured Numerical Data Using Text Mining." Applied Sciences, 7(11).
Li, S., Cai, H., and Kamat, V. R. (2016). "Integrating Natural Language Processing and Spatial Reasoning for Utility Compliance Checking." Journal of Construction Engineering and Management, 142(12), 04016074.
Li, Z., Yang, Z., Shen, C., Xu, J., Zhang, Y., and Xu, H. (2019). "Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text." BMC medical informatics and decision making, 19(1), 22.
Liu, Y., Wei, F., Li, S., Ji, H., Zhou, M., and Wang, H. (2015). "A dependency-based neural network for relation classification." arXiv preprint arXiv:1507.04646.
Motawa, I. A., Anumba, C. J., and El-Hamalawi, A. (2006). "A fuzzy system for evaluating the risk of change in construction projects." Advances in Engineering Software, 37(9), 583-591.
Pawar, S., Palshikar, G. K., and Bhattacharyya, P. (2017). "Relation extraction: A survey." arXiv preprint arXiv:1712.05191.
Roth, M., and Lapata, M. (2016). "Neural semantic role labeling with dependency path embeddings." arXiv preprint arXiv:1605.07515.
Suakkaphong, N., Zhang, Z., and Chen, H. (2011). "Disease named entity recognition using semisupervised learning and conditional random fields." Journal of the American Society for Information Science and Technology, 62(4), 727-737.
Wu, Y., Zhang, Q., Huang, X., and Wu, L. (2009). "Phrase dependency parsing for opinion mining." Proc., Proceedings of the 2009 conference on empirical methods in natural language processing, 3(3), 1533-1541.
Zhang, F., Fleyeh, H., Wang, X., and Lu, M. (2019). "Construction site accident analysis using text mining and natural language processing techniques." Automation in Construction, 99, 238-248.
Zhang, J., and El-Gohary, N. (2012). "Automated regulatory information extraction from building codes: Leveraging syntactic and semantic information." Proc., Construction Research Congress 2012: Construction Challenges in a Flat World, 622-632.
Zhang, J., and El-Gohary, N. M. (2013). "Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking." Journal of Computing in Civil Engineering, 30(2), 04015014.
Zhang, J., and El-Gohary, N. M. (2017). "Semantic-Based Logic Representation and Reasoning for Automated Regulatory Compliance Checking." Journal of Computing in Civil Engineering, 31(1), 04016037.
Zhang, Y., Zheng, W., Lin, H., Wang, J., Yang, Z., and Dumontier, M. (2017). "Drug–drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths." Bioinformatics, 34(5), 828-835.
Zhou, P., and El-Gohary, N. (2015). "Ontology-Based Information Extraction from Environmental Regulations for Supporting Environmental Compliance Checking." Computing in Civil Engineering 2015, 190-198.
Zhou, P., and El-Gohary, N. (2017). "Ontology-based automated information extraction from building energy conservation codes." Automation in Construction, 74, 103-117.
Zwicklbauer, S., Seifert, C., and Granitzer, M. (2015). "From general to specialized domain: Analyzing three crucial problems of biomedical entity disambiguation." Proc., Database and Expert Systems Applications, 76-93.
Information & Authors
Information
Published In
Construction Research Congress 2020: Project Management and Controls, Materials, and Contracts
Pages: 134 - 142
Editors: David Grau, Ph.D., Arizona State University, Pingbo Tang, Ph.D., Arizona State University, and Mounir El Asmar, Ph.D., Arizona State University
ISBN (Online): 978-0-7844-8288-9
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Nov 9, 2020
Published in print: Nov 9, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.