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

被引：12

作者：

Hong, Xudong ^{[1
]}

Zheng, Xiao ^{[1
]}

Xia, Jinyuan ^{[1
]}

Wei, Linna ^{[1
]}

Xue, Wei ^{[1
]}

机构：

[1] Anhui Univ Technol, Maanshan 243002, Peoples R China

来源：

CMC-COMPUTERS MATERIALS & CONTINUA | 2019年 / 58卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Non-ferrous metal; CNN; cross-lingual; text classification; word vector;

D O I：

10.32604/cmc.2019.04059

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：379 / 389

页数：11

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