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Deep learning-based methods for natural hazard named entity recognition.

Junlin Sun1, Yanrong Liu1, Jing Cui1

  • 1School of Resources and Environment, Anhui Agricultural University, Hefei, 230036, China.

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This study introduces a deep learning model for natural hazard named entity recognition, improving information extraction for disaster mitigation. The XLNet-BiLSTM-CRF model achieves high accuracy in identifying natural hazard entities from text.

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Area of Science:

  • Natural Language Processing
  • Disaster Management
  • Artificial Intelligence

Background:

  • Natural hazard named entity recognition is crucial for disaster mitigation but faces challenges like entity variability.
  • Existing methods struggle with the dynamic and diverse nature of natural hazard information.

Purpose of the Study:

  • To develop an effective deep learning method for natural hazard named entity recognition.
  • To improve the acquisition of natural hazard information for better disaster mitigation strategies.

Main Methods:

  • Construction of a natural disaster annotated corpus for model training and evaluation.
  • Comparison of deep learning methods utilizing word vector features, focusing on pretraining, feature extraction, and decoding.
  • Proposal and implementation of the XLNet-BiLSTM-CRF model for natural hazard named entity recognition.

Main Results:

  • The proposed XLNet-BiLSTM-CRF model achieved high performance with a precision of 92.80%, recall of 91.74%, and F1-score of 92.27%.
  • The model demonstrated superior effectiveness compared to other evaluated methods in recognizing natural hazard named entities.
  • Identified research hotspots in natural hazards literature over the past decade using the developed model.

Conclusions:

  • The XLNet-BiLSTM-CRF model offers a robust and effective solution for natural hazard named entity recognition.
  • Deep learning approaches can automate feature extraction, reducing reliance on manual rules in this domain.
  • The method facilitates efficient information extraction, supporting natural hazard mitigation efforts.