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Izvorni znanstveni članak
https://doi.org/10.17559/TV-20190623122323

News Text Classification Based on an Improved Convolutional Neural Network

Wenjing Tao ; Beijing Jiaotong University, No. 3, Shangyuan Village, Haidian District, Beijing, China
Dan Chang ; Beijing Jiaotong University, No. 3, Shangyuan Village, Haidian District, Beijing, China

Puni tekst: engleski, pdf (1 MB) str. 1400-1409 preuzimanja: 57* citiraj
APA 6th Edition
Tao, W. i Chang, D. (2019). News Text Classification Based on an Improved Convolutional Neural Network. Tehnički vjesnik, 26 (5), 1400-1409. https://doi.org/10.17559/TV-20190623122323
MLA 8th Edition
Tao, Wenjing i Dan Chang. "News Text Classification Based on an Improved Convolutional Neural Network." Tehnički vjesnik, vol. 26, br. 5, 2019, str. 1400-1409. https://doi.org/10.17559/TV-20190623122323. Citirano 15.11.2019.
Chicago 17th Edition
Tao, Wenjing i Dan Chang. "News Text Classification Based on an Improved Convolutional Neural Network." Tehnički vjesnik 26, br. 5 (2019): 1400-1409. https://doi.org/10.17559/TV-20190623122323
Harvard
Tao, W., i Chang, D. (2019). 'News Text Classification Based on an Improved Convolutional Neural Network', Tehnički vjesnik, 26(5), str. 1400-1409. https://doi.org/10.17559/TV-20190623122323
Vancouver
Tao W, Chang D. News Text Classification Based on an Improved Convolutional Neural Network. Tehnički vjesnik [Internet]. 2019 [pristupljeno 15.11.2019.];26(5):1400-1409. https://doi.org/10.17559/TV-20190623122323
IEEE
W. Tao i D. Chang, "News Text Classification Based on an Improved Convolutional Neural Network", Tehnički vjesnik, vol.26, br. 5, str. 1400-1409, 2019. [Online]. https://doi.org/10.17559/TV-20190623122323

Sažetak
With the explosive growth in Internet news media and the disorganized status of news texts, this paper puts forward an automatic classification model for news based on a Convolutional Neural Network (CNN). In the model, Word2vec is firstly merged with Latent Dirichlet Allocation (LDA) to generate an effective text feature representation. Then when an attention mechanism is combined with the proposed model, higher attention probability values are given to key features to achieve an accurate judgment. The results show that the precision rate, the recall rate and the F1 value of the model in this paper reach 96.4%, 95.9% and 96.2% respectively, which indicates that the improved CNN, through a unique framework, can extract deep semantic features of the text and provide a strong support for establishing an efficient and accurate news text classification model.

Ključne riječi
attention mechanism; Convolutional Neural Network (CNN); feature representation; text classification; Word2vec

Hrčak ID: 226037

URI
https://hrcak.srce.hr/226037

Posjeta: 96 *