A Long-Text Classification Method of Chinese News Based on BERT and CNN

Chen, Xinying; Cong, Peimin; Lv, Shuo

doi:10.1109/access.2022.3162614

Cited by 66 publications

(27 citation statements)

References 32 publications

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“…This network selects features by utilizing the convolution layer (CL) through a convolution kernel [63]. Its application to text classification was first introduced by [64] and produced impressive results. Since then, the algorithm has proven to be highly effective in various classification scenarios, including student learning needs [65], news story categorization [66], and Arabic text classification [67].…”

Section: Deep Learning Classifiermentioning

confidence: 99%

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority oversampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

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Section: Deep Learning Classifiermentioning

confidence: 99%

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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“…with many feature selection techniques and K-Nearest Neighbor classifier works well only in the cases, when the feature selection techniques either Information Gain (IG) or Mutual Information (MI). To improve the accuracy of long text classification of Chinese news, Chen et al 26 propose a BERT-based local feature convolutional network (LFCN) model including four novel modules. Liang et al 27 gave an improved ensemble model for Chinese text classification based on CNN and RNN structure.…”

Section: Related Workmentioning

confidence: 99%

Feature-enhanced text-inception model for Chinese long text classification

Guo

Jiayu

et al. 2023

Sci Rep

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To solve the problem regarding unbalanced distribution of multi-category Chinese long texts and improve the classification accuracy thereof, a data enhancement method was proposed. Combined with this method, a feature-enhanced text-inception model for Chinese long text classification was proposed. First, the model used a novel text-inception module to extract important shallow features of the text. Meanwhile, the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network were employed to form a deep feature extraction module to understand the semantic information in the text; K-MaxPooling was then used to reduce the dimension of its shallow and deep features and enhance the overall features. Finally, the Softmax function was used for classification. By comparing the classification effects with a variety of models, the results show that the model can significantly improve the accuracy of long Chinese text classification and has a strong ability to recognize long Chinese text features. The accuracy of the model is 93.97% when applied to an experimental dataset.

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“…Compared with models that extract features manually, convolutional neural networks have stronger discriminative and generalization capabilities. It can effectively obtain the local features of the data to be tested, and is widely used in classification problems such as image processing, speech recognition, and natural language processing (Maite et al, 2020;Tian, 2020;Wang et al, 2021;Chen et al, 2022;Sultana et al, 2022).…”

Section: Analysis Of Particle Swarm Optimization Fusion Convolutional...mentioning

confidence: 99%

Bearing fault diagnosis based on particle swarm optimization fusion convolutional neural network

Liu

Wang

et al. 2022

Front. Neurorobot.

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Bearings are the most basic and important mechanical parts. The stable and safe operation of the equipment requires bearing fault diagnosis in advance. So, bearing fault diagnosis is an important technology. However, the feature extraction quality of the traditional convolutional neural network bearing fault diagnosis is not high and the recognition accuracy will decline under different working conditions. In response to these questions, a bearing fault model based on particle swarm optimization (PSO) fusion convolution neural network is proposed in this paper. The model first adaptively adjusts the hyperparameters of the model through PSO, then introduces residual connections to prevent the gradient from disappearing, uses global average pooling to replace the fully connected layer to reduce the training parameters of the model, and finally adds a dropout layer to prevent network overfitting. The experimental results show that the model is under four conditions, two of which can achieve 100% recognition, and the other two can also achieve more than 98% accuracy. And compared with the traditional diagnosis method, the model has higher accuracy under variable working conditions. This research has important research significance and economic value in the field of the intelligent machinery industry.

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A Long-Text Classification Method of Chinese News Based on BERT and CNN

Cited by 66 publications

References 32 publications

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

Feature-enhanced text-inception model for Chinese long text classification

Bearing fault diagnosis based on particle swarm optimization fusion convolutional neural network

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