NDLI: A Military Named Entity Recognition Method based on pre-training language model and BiLSTM-CRF

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A Military Named Entity Recognition Method based on pre-training language model and BiLSTM-CRF

Content Provider	Scilit
Author	Lu, Yiwei Yang, Ruopeng Jiang, Xuping Yin, Changsheng Song, Xiaoyu
Copyright Year	2020
Description	Journal: Journal of Physics: Conference Series Military named entity recognition is the basis of the military intelligence analysis and operational information service. In order to solve the problems of inaccurate word segmentation, diverse forms and the lack of corpus in military texts, the author proposes a method of military named entity recognition based on Pre-training language model. On this basis, and taking advantage of Bi-directional Long Short-Term Memory (BiLSTM) neural network in dealing with the wide range of contextual information, the BERT-BiLSTM-CRF named entity recognition model was constructed. The experimental results on the tagged military text corpus show that the extraction effect of this method is better than that of the traditional methods.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1693/1/012161/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1693/1/012161
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1693
Language	English
Publisher	IOP Publishing
Publisher Date	2020-12-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Hardware and Architecture Named Entity Recognition
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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