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Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military

Author

Listed:
  • Fei Liao

    (College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China)

  • Liangli Ma

    (College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China)

  • Jingjing Pei

    (Force 91001, Beijing 100841, China)

  • Linshan Tan

    (Force 91001, Beijing 100841, China)

Abstract

Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

Suggested Citation

  • Fei Liao & Liangli Ma & Jingjing Pei & Linshan Tan, 2019. "Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military," Future Internet, MDPI, vol. 11(8), pages 1-11, August.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:8:p:180-:d:258713
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    References listed on IDEAS

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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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