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Event Recognition Based on Deep Learning in Chinese Texts.

Yajun Zhang1, Zongtian Liu1, Wen Zhou1

  • 1Shanghai University, School of Computer Engineering and Science, Shanghai, China.

Plos One
|August 9, 2016
PubMed
Summary
This summary is machine-generated.

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This study introduces a deep learning model for Chinese emergency event recognition, achieving an 85.17% F-measure. A novel dynamic-supervised deep belief network further boosts performance to 88.11% with minimal training time increase.

Area of Science:

  • Natural Language Processing
  • Deep Learning
  • Information Extraction

Background:

  • Traditional rule-based and shallow neural network methods for event recognition face limitations in feature extraction and precision.
  • Existing methods struggle with the complexity and nuances of event recognition in Chinese emergency contexts.

Purpose of the Study:

  • To propose a novel deep learning model, the Chinese Emergency Event Recognition Model (CEERM), for improved event recognition.
  • To enhance the performance and efficiency of deep belief networks (DBNs) for event recognition tasks.

Main Methods:

  • Utilized a word segmentation system and classified words into five categories: trigger words, participants, objects, time, and location.
  • Engineered a feature vector set incorporating part of speech, dependency grammar, length, location, trigger word distance, and frequency.

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  • Employed a deep belief network (DBN) for deep semantic feature extraction and a backpropagation neural network for trigger word identification.

    • Main Results:

    • The CEERM achieved a maximum F-measure of 85.17% in event recognition.
    • The proposed dynamic-supervised DBN improved the F-measure to 88.11% while managing training time increases.
    • The dynamic-supervised DBN demonstrated effective control over training time alongside performance gains.

    Conclusions:

    • The CEERM offers a significant advancement in Chinese emergency event recognition.
    • Dynamic-supervised DBNs represent a promising approach for enhancing deep learning models in event recognition.
    • The developed methods show potential for practical applications in emergency response and information systems.