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

Fei Liao, Liangli Ma, Jingjing Pei and Linshan Tan
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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

Future Internet, 2019, vol. 11, issue 8, 1-11

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.

Keywords: military named entity recognition; self-attention mechanism; BiLSTM (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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