Deep learning-based methods for natural hazard named entity recognition

Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The meth...

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Bibliographic Details
Published in:Scientific reports 2022-03, Vol.12 (1), p.4598-4598, Article 4598
Main Authors: Sun, Junlin, Liu, Yanrong, Cui, Jing, He, Handong
Format: Article
Language:English
Subjects:
639/705/117
639/705/531
704/4111
Data Collection
Deep Learning
Electronic Health Records
Humanities and Social Sciences
Language
multidisciplinary
Names
Natural disasters
Natural Language Processing
Science
Science (multidisciplinary)
Space debris
Training
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Summary:Natural hazard named entity recognition is a technique used to recognize natural hazard entities from a large number of texts. The method of natural hazard named entity recognition can facilitate acquisition of natural hazards information and provide reference for natural hazard mitigation. The method of named entity recognition has many challenges, such as fast change, multiple types and various forms of named entities. This can introduce difficulties in research of natural hazard named entity recognition. To address the above problem, this paper constructed a natural disaster annotated corpus for training and evaluation model, and selected and compared several deep learning methods based on word vector features. A deep learning method for natural hazard named entity recognition can automatically mine text features and reduce the dependence on manual rules. This paper compares and analyzes the deep learning models from three aspects: pretraining, feature extraction and decoding. A natural hazard named entity recognition method based on deep learning is proposed, namely XLNet-BiLSTM-CRF model. Finally, the research hotspots of natural hazards papers in the past 10 years were obtained through this model. After training, the precision of the XLNet-BilSTM-CRF model is 92.80%, the recall rate is 91.74%, and the F1-score is 92.27%. The results show that this method, which is superior to other methods, can effectively recognize natural hazard named entities.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-08667-2