Deep Learning-Based Coreference Resolution for Bridge Report Analytics
Author:
Affiliation:
1. Ph.D. Student, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL.
2. Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, Urbana, IL.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784485262.026
Reference20 articles.
1. Integrated condition rating and forecasting method for bridge decks using Visual Inspection and Ground Penetrating Radar
2. ASCE. Report card for America’s infrastructure https://infrastructurereportcard.org/wp-content/uploads/2020/12/Bridges-2021.pdf.
3. Bridge Infrastructure Asset Management System: Comparative Computational Machine Learning Approach for Evaluating and Predicting Deck Deterioration Conditions
4. Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle;Cao Y. T.;CL,2021
5. Devlin J. Chang M. W. Lee K. and Toutanova K. (2019). “BERT: Pre-training of deep bidirectional transformers for language understanding.” Proc. NAACL HLT 2019-2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1 4171–4186.
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