Multilingual document classification via transductive learning

Salvatore Romeo, Dino Ienco, Andrea Tagarelli

Research output: Contribution to journalConference article

1 Citation (Scopus)


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.

Original languageEnglish
JournalCEUR Workshop Proceedings
Publication statusPublished - 1 Jan 2015
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)

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