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

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.