Joint Learning With BERT-GCN and Multi-Attention for Event Text Classification and Event Assignment

She, Xiangrong; Chen, Jianpeng; Chen, Gang

doi:10.1109/access.2022.3156918

Cited by 14 publications

(4 citation statements)

References 29 publications

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“…ii ij j DA =  is the degree matrix and (0) (1) , WW are trainable input-to-hidden weight matrix and hidden-to-output weight matrix respectively.…”

Section: â D Admentioning

confidence: 99%

“…To evaluate the performance of the proposed model, five widely-used evaluation metrics are employed. Precision: precision focuses on the proportion of correctly predict labels in predicted set, its calculation formula is as follows 1 1…”

Section: B Evaluation Metricsmentioning

confidence: 99%

“…For a hotline operator, he analyzes the event text and labels the event. Traditional text classification methods mainly focus on a single label and fit poorly for the event text of the Chinese government hotline as a hotline event may have multiple labels [1]. Furthermore, the event text presents continuous expansion characteristics that cover all aspects of urban life and realize intelligent event classification.…”

Section: Introductionmentioning

confidence: 99%

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GCN-BERT and Memory Network Based Multi-Label Classification for Event Text of the Chinese Government Hotline

Bin

2022

IEEE Access

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In order to automatically generate multiple labels for the event text of the Chinese government hotline, this paper propose a multi-label classification framework based on graph convolutional network (GCN), BERT, and memory network. The framework consists of three modules: label count prediction module, label semantic insert module, and label selection module. In the label count prediction module, this paper constructs the event graph with the abstract meaning representation (AMR) and extract the event topic information vector with GCN. To predict the label count, this paper first use BERT to extract the event semantic information vector and then fuse it with the event topic information vector (GCN-BERT fusion vector) with a dynamic fusion gate. In the label semantic insert module, to obtain the event label candidate set, this paper uses a multi-hop memory network to store the event label semantic information, and then use the answer selection framework, which matches the GCN-BERT fusion vector with the event label semantic memory vector. In label selection module, this paper uses the label count based multi-label selection to sort the event label candidate set and guide to output the optimal multi-label set of the event. Comparison experimental results show that the proposed framework outperforms all baselines and ablation studies demonstrate the effectiveness of each module.

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“…ii ij j DA =  is the degree matrix and (0) (1) , WW are trainable input-to-hidden weight matrix and hidden-to-output weight matrix respectively.…”

Section: â D Admentioning

confidence: 99%

Section: B Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GCN-BERT and Memory Network Based Multi-Label Classification for Event Text of the Chinese Government Hotline

Bin

2022

IEEE Access

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“…Some scholars have also applied BERTGCN to engineering in various industries. XR et al used GCN and BERT to perform a joint learning method for text classification and event assignment on the Chinese government hotline [ 20 ]. The experimental results showed that the method could achieve better performance.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

Yang

Duan

Xiao

et al. 2022

IJERPH

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Nowadays, tourists increasingly prefer to check the reviews of attractions before traveling to decide whether to visit them or not. To respond to the change in the way tourists choose attractions, it is important to classify the reviews of attractions with high precision. In addition, more and more tourists like to use emojis to express their satisfaction or dissatisfaction with the attractions. In this paper, we built a dataset for Chinese attraction evaluation incorporating emojis (CAEIE) and proposed an explicitly n-gram masking method to enhance the integration of coarse-grained information into a pre-training (ERNIE-Gram) and Text Graph Convolutional Network (textGCN) (E2G) model to classify the dataset with a high accuracy. The E2G preprocesses the text and feeds it to ERNIE-Gram and TextGCN. ERNIE-Gram was trained using its unique mask mechanism to obtain the final probabilities. TextGCN used the dataset to construct heterogeneous graphs with comment text and words, which were trained to obtain a representation of the document output category probabilities. The two probabilities were calculated to obtain the final results. To demonstrate the validity of the E2G model, this paper was compared with advanced models. After experiments, it was shown that E2G had a good classification effect on the CAEIE dataset, and the accuracy of classification was up to 97.37%. Furthermore, the accuracy of E2G was 1.37% and 1.35% ahead of ERNIE-Gram and TextGCN, respectively. In addition, two sets of comparison experiments were conducted to verify the performance of TextGCN and TextGAT on the CAEIE dataset. The final results showed that ERNIE and ERNIE-Gram combined TextGCN and TextGAT, respectively, and TextGCN performed 1.6% and 2.15% ahead. This paper compared the effects of eight activation functions on the second layer of the TextGCN and the activation-function-rectified linear unit 6 (RELU6) with the best results based on experiments.

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