Labeled Bilingual Topic Model for Cross-Lingual Text Classification and Label Recommendation

Aiming at the increasingly rich multi language information resources and multi-label data in news reports and scientific literatures, in order to mining the relevance between languages and the correlation between data, this paper proposed labeled bilingual topic model, applied on cross-lingual text classification and label recommendation. First of all, it could assume that the keywords in the scientific literature are relevant to the abstract in same article, then extracted the keywords and regarded it as labels, and aligned the labels with topics in topic model, instantiated the "latent" topic. Secondly, trained the abstracts in article through the topic model proposed by this paper. Finally, classified the new documents by cross-lingual text classifier, also recommended the labels. The experiment result show that Micro-F1 measure reaches 94.81% in cross-lingual text classification task, and the recommended labels also reflects the sematic relevance with documents.