Named entity recognition of military equipment based on BERT-BILSTM-CRF model

Liping Yang; Qiqing Fang; Peng Zhang

doi:10.1117/12.2668327

20 April 2023 Named entity recognition of military equipment based on BERT-BILSTM-CRF model

Liping Yang, Qiqing Fang, Peng Zhang

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260219 (2023) https://doi.org/10.1117/12.2668327
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Named entity recognition of military equipment is an important task in the construction of knowledge graph in the military domain. It is a key technical means to improve the intelligence degree of military intelligence information retrieval, intelligence analysis, command and decision. There are many problems in the task of named entity recognition in the field of military equipment, such as fuzzy entity boundary, complex grammar structure and many professional words, which directly lead to the loss of accuracy in named entity recognition. Aiming at the above challenges, this paper proposes a BERT- BILSTM -CRF neural network model based on type labeling and part-of-speech labeling of entities in the field of military equipment. BERT's pre-trained language model fully considers the correlation between words when constructing word vectors, which is used to supplement the semantic relations embedded in words, and can solve the fuzzy problem in name entity recognition. BILSTM layer is used to carry out bidirectional semantic coding, which can solve the long-distance dependence problem. Finally, the output of BERT-BILSTM layer is decoded by CRF layer, and the optimal tag sequence is obtained. The experimental results show that compared with CRF model and BILSTM-CRF model, the F1 value of the proposed model is increased by 10%.Compared with the BILSTM-Attention-CRF model, the F value of this model increased by 10.48%, and the recall rate increased by 15.02%.Compared with the BERT-IDCNN-CRF model, the F value of this model is increased by 0.62%, and the recall rate is increased by 4.55%.

Citation Download Citation

Liping Yang, Qiqing Fang, and Peng Zhang "Named entity recognition of military equipment based on BERT-BILSTM-CRF model", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260219 (20 April 2023); https://doi.org/10.1117/12.2668327

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Instrument modeling

Education and training

Data modeling

Matrices

Semantics

Feature extraction

Technology

Show All Keywords

Keywords/Phrases

Search In:

Publication Years