Citation

Song D, Yang L, Zhong S. China Saf. Sci. J. 2022; 32(2): 115-120.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.02.016

PMID

unavailable

Abstract

In order to quickly and precisely extract triplet information from text on online social media, NLP technology was utilized to study the application and algorithm optimization of the information extraction. Then, BERT pre-trained language model was applied in a case of triplet information extraction of geological disasters. Considering the model 's problems of " low-rank bottlenecks" caused by its own MHA mechanism, the key-size was increased to optimize the model. The results show that the proposed method can significantly improve fault tolerance and accuracy of disaster information extraction, including disaster type, occurrence location, occurrence time, from news reports. And it can be used to analyze spatial distribution and trend of disasters, and then provide scientific analysis and decision-making support for disaster emergency management, such as preparation of emergency plan, optimal allocation of emergency resources, regional monitoring and early warning, etc. © 2022, Editorial Department of China Safety Science Journal. All rights reserved.

Language: zh