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

Lu, Yiwei; Jiang, Xuping; Yin, Changsheng; Song, Xiaoyu

doi:10.1088/1742-6596/1693/1/012161

Cited by 7 publications

(3 citation statements)

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“…The emergence of question and answer (Q&A) systems presents an opportunity to overcome this issue. Named entity recognition (NER) is now widely used in the military ( Wang et al., 2018 ; Lu et al., 2020 ; Baigang and Yi, 2023 ; Li et al., 2023 ), entertainment and culture ( Molina-Villegas et al., 2021 ; Zhuang et al., 2021 ; Fu et al., 2022 ; Huang et al., 2022 ), cybersecurity ( Georgescu et al., 2019 ; Simran et al., 2020 ; Chen et al., 2021 ; Ma et al., 2021 ), and medicine ( Ji et al., 2019 ; Li et al., 2020 ; Wang et al., 2020 ; Liu et al., 2022 ). However, the application of NER in the agricultural sector is still in the early stages of development ( Wang et al., 2022 ; Yu et al., 2022a ; Qian et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Yu,

Wang,

Xue

2024

Front. Plant Sci.

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IntroductionThe diversity of edible fungus species and the extent of mycological knowledge pose significant challenges to the research, cultivation, and popularization of edible fungus. To tackle this challenge, there is an urgent need for a rapid and accurate method of acquiring relevant information. The emergence of question and answer (Q&A) systems has the potential to solve this problem. Named entity recognition (NER) provides the basis for building an intelligent Q&A system for edible fungus. In the field of edible fungus, there is a lack of a publicly available Chinese corpus suitable for use in NER, and conventional methods struggle to capture long-distance dependencies in the NER process.MethodsThis paper describes the establishment of a Chinese corpus in the field of edible fungus and introduces an NER method for edible fungus information based on XLNet and conditional random fields (CRFs). Our approach combines an iterated dilated convolutional neural network (IDCNN) with a CRF. First, leveraging the XLNet model as the foundation, an IDCNN layer is introduced. This layer addresses the limited capacity to capture features across utterances by extending the receptive field of the convolutional kernel. The output of the IDCNN layer is input to the CRF layer, which mitigates any labeling logic errors, resulting in the globally optimal labels for the NER task relating to edible fungus.ResultsExperimental results show that the precision achieved by the proposed model reaches 0.971, with a recall of 0.986 and an F1-score of 0.979.DiscussionThe proposed model outperforms existing approaches in terms of these evaluation metrics, effectively recognizing entities related to edible fungus information and offering methodological support for the construction of knowledge graphs.

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Section: Introductionmentioning

confidence: 99%

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Yu,

Wang,

Xue

2024

Front. Plant Sci.

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“…Military entity recognition technology is based on general entity recognition technology, which has experienced the development process from dictionary, rule, and machine learning to deep learning [3]. At present, the recognition model based on pretraining language model and deep learning algorithm is the main stream of entity recognition in military and general fields with the increasing computing power of small computers, the maturity of deep learning technology, and the development of pretraining language model [4]. However, it is a challenge work for the military entity recognition with the distinct domain characteristics of military field.…”

Section: Introductionmentioning

confidence: 99%

Fusion Deep Learning and Machine Learning for Heterogeneous Military Entity Recognition

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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With respect to the fuzzy boundaries of military heterogeneous entities, this paper improves the entity annotation mechanism for entity with fuzzy boundaries based on related research works. This paper applies a BERT-BiLSTM-CRF model fusing deep learning and machine learning to recognize military entities, and thus, we can construct a smart military knowledge base with these entities. Furthermore, we can explore many military AI applications with the knowledge base and military Internet of Things (MIoT). To verify the performance of the model, we design multiple types of experiments. Experimental results show that the recognition performance of the model keeps improving with the increasing size of the corpus in the multidata source scenario, with the F -score increasing from 73.56% to 84.53%. Experimental results of cross-corpus cross-validation show that the more types of entities covered in the training corpus and the richer the representation type, the stronger the generalization ability of the trained model, in which the recall rate of the model trained with the novel random type corpus reaches 74.33% and the F -score reaches 76.98%. The results of the multimodel comparison experiments show that the BERT-BiLSTM-CRF model applied in this paper performs well for the recognition of military entities. The longitudinal comparison experimental results show that the F -score of the BERT-BiLSTM-CRF model is 18.72%, 11.24%, 9.24%, and 5.07% higher than the four models CRF, LSTM-CRF, BiLSTM-CR, and BERT-CRF, respectively. The cross-sectional comparison experimental results show that the F -score of the BERT-BiLSTM-CRF model improved by 6.63%, 7.95%, 3.72%, and 1.81% compared to the Lattice-LSTM-CRF, CNN-BiLSTM-CRF, BERT-BiGRU-CRF, and BERT-IDCNN-CRF models, respectively.

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“…Wu et al (2020) [5] put forward an attention-based multi-task NN (Neural Network) text classification and sequence labeling model and applied it to the NER and intention analysis of Chinese online medical problems. Lu et al (2020) [6] projected a military NER method based on a pre-training language model. Experimental results on labeled military text corpus showed that the proposed method outperformed the traditional method.…”

Section: Introductionmentioning

confidence: 99%

Named Entity Recognition in Equipment Support Field Using Tri-Training Algorithm and Text Information Extraction Technology

Liu

et al. 2021

IEEE Access

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NER (Named Entity Recognition) is of great significance for the construction of a knowledge map. The purpose is to guarantee the recognition effect of named entity recognition method in the application scenario of vertical field, a named entity recognition method is proposed based on BI-LSTM-CRF [BI(Bidirectional) LSTM (Long-Short Term Memory) CRF (Conditional Random Field)] for equipment support field, which improves the recognition effect of the domain named entity and provides technical support for the subsequent construction of domain knowledge map. First, Chinese characters are represented by word embedding and input into the model. Then, the input feature vector sequence is processed through BI-LSTM NN (Neural Network) to extract contextual semantic learning features. Finally, the learned features are connected to the linear CRF, the NEs (Named Entity) in the field of equipment support are labeled, and the NER results are obtained and output. The experimental results show that the precision of the named entity recognition method based on the BI-LSTM-CRF model has reached 92.02%, the recall rate has reached 93.21%, and the F1score has reached 93.88%. Meanwhile, the performance of the proposed BI-LSTM-CRF model is higher than the precision of the BI-LSTM NN model and LSTM-CRF NN model.

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

Cited by 7 publications

References 5 publications

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Improved XLNet modeling for Chinese named entity recognition of edible fungus

Fusion Deep Learning and Machine Learning for Heterogeneous Military Entity Recognition

Named Entity Recognition in Equipment Support Field Using Tri-Training Algorithm and Text Information Extraction Technology

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