Table of Content

Open AccessOpen Access


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.


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.
  • 2624


  • 1217


  • 0


Share Link