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Open AccessArticle

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

1
College of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China
2
Force 91001, Beijing 100841, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(8), 180; https://doi.org/10.3390/fi11080180
Received: 15 July 2019 / Revised: 9 August 2019 / Accepted: 15 August 2019 / Published: 18 August 2019
(This article belongs to the Section Network Virtualization and Edge/Fog Computing)
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. View Full-Text
Keywords: military named entity recognition; self-attention mechanism; BiLSTM military named entity recognition; self-attention mechanism; BiLSTM
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Liao, F.; Ma, L.; Pei, J.; Tan, L. Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military. Future Internet 2019, 11, 180.

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