Multilingual document classification via transductive learning
Salvatore Osvaldo Romeo, Dino Ienco, Andrea Tagarelli · HAL (Le Centre pour la Communication Scientifique Directe) · 2015
We present a transductive learning based framework for multilingual document classification, originally proposed in [7]. A key aspect in our approach is the use of a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. Results on real-world multilingual corpora have highlighted the superiority of the proposed document model against existing language-dependent representation approaches, and the significance of the transductive setting for multilingual document classification.