Paper
20 April 2023 Named entity recognition of military equipment based on BERT-BILSTM-CRF model
Liping Yang, Qiqing Fang, Peng Zhang
Author Affiliations +
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%.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
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KEYWORDS
Instrument modeling

Education and training

Data modeling

Matrices

Semantics

Feature extraction

Technology

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