Naming entity recognition of citrus pests and diseases based on the BERT-BiLSTM-CRF model

Liu, Yafei; Wei, Siqi; Huang, Haijun; Lai, Quirino; Li, Mengshan; Guan, Lixin

doi:10.1016/j.eswa.2023.121103

Cited by 21 publications

(6 citation statements)

References 13 publications

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“…Chang et al [38] concatenated CRF with pretrained BERT models for NER tasks. Liu et al [39] proposed the BERT-BiLSTM-CRF recognition method and applied it to research on citrus pests and diseases. Gan et al [40] proposed the BERT-Transformer-BiLSTM-CRF model to handle the challenges posed by pronouns and polysemous words in Chinese NER.…”

Section: Methods Based On Deep Learningmentioning

confidence: 99%

LB-BMBC: MHBiaffine-CNN to Capture Span Scores with BERT Injected with Lexical Information for Chinese NER

Guo,

Zhang

2024

Int J Comput Intell Syst

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A substantial body of research has shown that introducing lexical information in Chinese Named Entity Recognition (NER) tasks can enhance the semantic and boundary information of Chinese words. However, in most methods, the introduction of lexical information occurs at the model architecture level, which cannot fully leverage the lexicon learning capability of pre-trained models. Therefore, we propose seamless integration of external Lexicon knowledge into the Transformer layer of BERT. Additionally, we have observed that in span-based recognition, adjacent spans have special spatial relationships. To capture this relationship, we extend the work after Biaffine and use Convolutional Neural Networks (CNN) to treat the score matrix as an image, allowing us to interact with the spatial relationships of spans. Our proposed LB-BMBC model was experimented on four publicly available Chinese NER datasets: Resume, Weibo, OntoNotes v4, and MSRA. In particular, during ablation experiments, we found that CNN can significantly improve performance.

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Section: Methods Based On Deep Learningmentioning

confidence: 99%

LB-BMBC: MHBiaffine-CNN to Capture Span Scores with BERT Injected with Lexical Information for Chinese NER

Guo,

Zhang

2024

Int J Comput Intell Syst

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“…As an illustration, Kameko et al [35] proposed a named entity recognition model combining BERT-CRF and multi-task learning for performing factual analysis of Japanese events. Liu et al [36] suggested a training model that uses Transformers' bi-directional encoder representation of BERT, combining BILSTM and CRF to extract specified entity classes from unstructured citrus pest and disease data. Chen et al [37] introduced the BERT-BILSTM-CRF model for extracting named entities from faulty text in power equipment.…”

Section: Knowledge Graph Constructionmentioning

confidence: 99%

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Xiao,

Yan,

Zhang

et al. 2024

Auton. Intell. Syst.

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The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

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“…Second, based on the OneRel model, the global dependency semantics and syntactic graph embedding representation of sentences are incorporated in the initial vector generation stage. Additionally, Bidirectional Encoder Representations from Transformers (BERT) and Bi-directional Long Short-Term Memory (Bi-LSTM) networks were used to extract semantic information from the original text [34,35]. This stage improves relational extraction by learning the spanning sentence syntactic structure features.…”

Section: Event Extraction Model Designmentioning

confidence: 99%

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

Liu,

Zhang,

Zheng

et al. 2023

Electronics

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Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.

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Naming entity recognition of citrus pests and diseases based on the BERT-BiLSTM-CRF model

Cited by 21 publications

References 13 publications

LB-BMBC: MHBiaffine-CNN to Capture Span Scores with BERT Injected with Lexical Information for Chinese NER

LB-BMBC: MHBiaffine-CNN to Capture Span Scores with BERT Injected with Lexical Information for Chinese NER

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

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