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Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers
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AI
DOI：10.1016/j.autcon.2021.104061.png)
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摘要
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Manual searching for infrastructure damage information from large amounts of textual data requires considerable time and effort. A fast and accurate collection of damage information from such data is necessary for effective infrastructure planning. In this study, a question answering method was proposed to provide users with infrastructure damage information from textual data automatically. The proposed method relies on a natural language model called bidirectional encoder representations from transformers for information retrieval. From the 143 reports collected from the National Hurricane Center, 533 question-answer pairs were formulated. The proposed model was trained with 435 pairs and tested with the remainder. The model was also tested with 43 question-answer pairs created using earthquake-related textual data and achieved F1-scores of 90.5% and 83.6% for the hurricane and earthquake datasets, respectively.
Keyword:
Bidirectional encoder representations from
transformers
Disaster management
Information retrieval
Infrastructure management
Natural language processing
Question answering
期刊
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11.5
论文数:
6.3K
被引数:
4.2W
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