Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2015 | IIR | Document classification,Transduction (machine learning),Language translation,Computer science,Document model,Conceptual space,Natural language processing,Artificial intelligence,Knowledge base |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salvatore Romeo | 1 | 27 | 5.32 |
| Dino Ienco | 2 | 295 | 42.01 |
| Andrea Tagarelli | 3 | 475 | 52.29 |