We present a transductive learning based framework for multilingual document classification, originally proposed in . 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.
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 1 Jan 2015|
ASJC Scopus subject areas
- Computer Science(all)