BiGRU attention capsule neural network for persian text classification

Kenarang, Amir; Farahani, Mehrdad; Manthouri, Mohammad

doi:10.1007/s12652-022-03742-y

Cited by 16 publications

(4 citation statements)

References 22 publications

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“…In text classification, the principle of neural network-based classification methods is to perform vector representation of news text through natural language processing techniques, and automate feature extraction of text based on neural networks, and finally form an end-to-end text classification model through classifiers [19][20]. The following is a brief description of each algorithm.…”

Section: Text Classification Methodsmentioning

confidence: 99%

Machine Learning Based News Text Classification

2020

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With the rapid development of the Internet era, the explosive growth of news data volume and the lack of effective management are gradually becoming serious problems, and it is increasingly difficult for readers to obtain valuable information quickly. How to quickly search for valuable information from the large amount of news text information is a meaningful task in text classification. Existing research methods still have some problems, such as directly combining the headline with the text, thus neglecting the importance of the headline, and the single model of classification, which leads to low classification results. For this reason, the main objective of this paper is to investigate the classification of news texts based on machine learning. This paper examines the current state of deep learning-based text classification and, in combination with the characteristics of news texts, chooses a machine learning-based text classification method to further explore and study news texts. Through the response time and core algorithm accuracy test of this system, the system better reflects the excellent performance of the system and meets the actual performance requirements of the system. The proposed news system can not only process system requests quickly, but also has excellent accuracy rate, which can better assist users to filter information and improve the user's experience of reading news.

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Section: Text Classification Methodsmentioning

confidence: 99%

Machine Learning Based News Text Classification

2020

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“…In summary, the signal recognition model of GIS equipment constructed in this paper adopts BiGRU neural network structure. Because BiGRU neural network has superior memory characteristics and bidirectional output, it can accurately and efficiently identify GIS device signal [10].…”

Section: Gis Device Signal Recognition Model Based On Improved Res-bi...mentioning

confidence: 99%

A three-dimensional dynamic modeling method for substation GIS equipment based on improved Res-BiGRU model

Zhang,

Chang,

Chang

et al. 2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…Chen et al [64] applied the graph convolutional network to learn label embeddings and the correlations between labels, a fusion layer to combine the label information with the contextual semantic information of texts, and the Capsnet to extract the spatial feature information of texts. Kenarang et al [65] combined the attention mechanism and the Capsnet method to obtain text topics in the Persian news corpus. Te results of the comparison show an improvement in the classifcation performance of the Persian texts.…”

Section: Related Workmentioning

confidence: 99%

CNFRD: A Few-Shot Rumor Detection Framework via Capsule Network for COVID-19

Chen

et al. 2023

International Journal of Intelligent Systems

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In recent years, COVID-19 has become the hottest topic. Various issues, such as epidemic transmission routes and preventive measures, have “occupied” several online social media platforms. Many rumors about COVID-19 have also arisen, causing public anxiety and seriously affecting normal social order. Identifying a rumor at its very inception is crucial to reducing the potential harm of its evolution to society as a whole. However, epidemic rumors provide limited signal features in the early stage. In order to identify rumors with data sparsity, we propose a few-shot learning rumor detection model based on capsule networks (CNFRD), utilizing the metric learning framework and the capsule network to detect the rumors posted during unexpected epidemic events. Specifically, we constructively use the capsule network neural layer to summarize the historical rumor data and obtain the generalized class representation based on the historical rumor data samples. Besides, we calculate the distance between the epidemic rumor sample and the historical rumor class-wise representation according to the metric module. Finally, epidemic rumors are discriminated against according to the nearest neighbor principle. The experimental results prove that the proposed method can achieve higher accuracy with fewer epidemic rumor samples. This approach provided 88.92% accuracy on the Chinese rumor dataset and 87.07% accuracy on the English rumor dataset, which improved by 7% to 23% over existing approaches. Therefore, the CNFRD model can identify epidemic rumors in COVID-19 as early as possible and effectively improve the performance of rumor detection.

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BiGRU attention capsule neural network for persian text classification

Cited by 16 publications

References 22 publications

Machine Learning Based News Text Classification

Machine Learning Based News Text Classification

A three-dimensional dynamic modeling method for substation GIS equipment based on improved Res-BiGRU model

CNFRD: A Few-Shot Rumor Detection Framework via Capsule Network for COVID-19

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