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Cross-Lingual Non-Ferrous Metals Related News Recognition Method Based on CNN with A Limited Bi-Lingual Dictionary

Xudong Hong1, Xiao Zheng1,*, Jinyuan Xia1, Linna Wei1, Wei Xue1
Anhui University of Technology, Maanshan, 243002, China.
* Corresponding Author: Xiao Zheng. Email: .

Computers, Materials & Continua 2019, 58(2), 379-389.


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.


Non-ferrous metal, CNN, cross-lingual, text classification, word vector.

Cite This Article

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," Computers, Materials & Continua, vol. 58, no.2, pp. 379–389, 2019.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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