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Intelligent Classification Method of Archive Data Based on Multigranular Semantics.

Xiaobo Jiang1

  • 1Jilin University of Architecture and Technology, Changchun, Jilin 130114, China.

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Summary
This summary is machine-generated.

This study introduces an intelligent archive data classification method using multigranular semantics. The proposed model enhances the efficiency and accuracy of archive management by analyzing text content for better data utilization.

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Area of Science:

  • Information Science
  • Computer Science
  • Data Science

Background:

  • The explosion of digital archive data necessitates improved intelligent management.
  • Current manual classification methods are inefficient and overlook inherent content information.
  • Analyzing content correlations is crucial for archive data discovery and utilization.

Purpose of the Study:

  • To propose an intelligent archive data classification method based on multigranular semantics.
  • To enhance the efficiency and accuracy of archive data management.
  • To improve the discovery and utilization of archive information.

Main Methods:

  • Construction of a semantic-label multigranular attention model.
  • Integration of stacked expanded convolutional coding and label graph attention modules.
  • Training the model on a multilabel dataset for convergence.

Main Results:

  • The developed model effectively classifies archive data based on multigranular semantics.
  • The attention mechanism refines label prediction accuracy.
  • The trained model provides accurate classification results for new archive data.

Conclusions:

  • The multigranular semantic-based intelligent classification method offers a significant improvement over manual approaches.
  • This approach enhances intelligent archive management by leveraging text content analysis.
  • The model facilitates better data mining, analysis, and utilization within digital archives.