Open Access

ARTICLE

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

Xudong Hong¹, Xiao Zheng^1,*, Jinyuan Xia¹, Linna Wei¹, Wei Xue¹

1 Anhui University of Technology, Maanshan, 243002, China.

* Corresponding Author: Xiao Zheng. Email: .

Computers, Materials & Continua 2019, 58(2), 379-389. https://doi.org/10.32604/cmc.2019.04059

Abstract

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.

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Citations