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Typhoon disaster state information extraction for Chinese texts.

Peng Ye1,2, Chunju Zhang3, Mingzhu Chen4,5

  • 1Urban Planning and Development Institute, Yangzhou University, Yangzhou, 225127, China.

Scientific Reports
|April 4, 2024
PubMed
Summary

This study introduces a typhoon disaster state information extraction (TDSIE) method for Chinese texts. TDSIE effectively integrates fragmented typhoon data, enabling comprehensive analysis of disaster dynamics and evolution.

Keywords:
Spatio-temporal semantic unitState information extractionState information fusionTyphoon disasterTyphoon disaster semantic vector

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Area of Science:

  • Geographical research
  • Disaster science
  • Natural language processing

Background:

  • Typhoon disasters have complex temporal evolutionary processes crucial for geographical research.
  • Current disaster information extraction methods struggle with dispersed data and spatio-temporal diversity.
  • Analyzing typhoon disaster states requires systematic integration of fragmented information.

Purpose of the Study:

  • To propose a typhoon disaster state information extraction (TDSIE) method for Chinese texts.
  • To facilitate the systematic integration of fragmented typhoon disaster information.
  • To support the analysis of typhoon disaster spatio-temporal patterns and evolutionary characteristics.

Main Methods:

  • Integration of part-of-speech tagging with spatio-temporal information extraction for text tagging.
  • Construction of typhoon disaster semantic vectors within spatio-temporal semantic units.
  • Co-referential state information fusion based on spatio-temporal cues.

Main Results:

  • The proposed TDSIE method achieves precision and recall rates consistently surpassing 85% on online news data.
  • TDSIE enables the systematic integration of fragmented typhoon disaster information.
  • The extracted information supports analysis of spatio-temporal patterns, evolutionary characteristics, and activity modes of typhoon disasters.

Conclusions:

  • TDSIE provides valuable support for investigating the inherent process properties of typhoon disasters.
  • The method enhances the understanding of typhoon disaster dynamics across various scales.
  • This approach addresses challenges posed by data dispersion and spatio-temporal diversity in disaster analysis.