To acquire non-ferrous metals related news from different countries’ internet, we proposed a cross-lingual non-ferrous metals related news recognition method based on CNN with a limited bilingual dictionary. Firstly, considering the lack of related language resources of non-ferrous metals, we use a limited bilingual dictionary and CCA to learn cross-lingual word vector and to represent news in different languages uniformly. Then, to improve the effect of recognition, we use a variant of the CNN to learn recognition features and construct the recognition model. The experimental results show that our proposed method acquires better results.
Cite This Article
APA Style
Hong, X., Zheng, X., Xia, J., Wei, L., Xue, W. (2019). Cross-lingual non-ferrous metals related news recognition method based on CNN with A limited bi-lingual dictionary. Computers, Materials & Continua, 58(2), 379-389. https://doi.org/10.32604/cmc.2019.04059
Vancouver Style
Hong X, Zheng X, Xia J, Wei L, Xue W. Cross-lingual non-ferrous metals related news recognition method based on CNN with A limited bi-lingual dictionary. Comput Mater Contin. 2019;58(2):379-389 https://doi.org/10.32604/cmc.2019.04059
IEEE Style
X. Hong, X. Zheng, J. Xia, L. Wei, and W. Xue "Cross-Lingual Non-Ferrous Metals Related News Recognition Method Based on CNN with A Limited Bi-Lingual Dictionary," Comput. Mater. Contin., vol. 58, no. 2, pp. 379-389. 2019. https://doi.org/10.32604/cmc.2019.04059