Name
Abstract | ||
|---|---|---|
This article deals with the problem of Cross-Lingual Text Categorization (CLTC), which arises when documents in different languages must be classified according to the same classification tree. We describe practical and cost-effective solutions for automatic Cross-Lingual Text Categorization, both in case a sufficient number of training examples is available for each new language and in the case that for some language no training examples are available. Experimental results of the bi-lingual classification of the ILO corpus (with documents in English and Spanish) are obtained using bi-lingual training, terminology translation and profile-based translation. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/b11967 | Lecture Notes in Computer Science |
Field | DocType | Volume |
CLTC,Categorization,Cross lingual,Query expansion,Computer science,Natural language processing,Language identification,Artificial intelligence,Constructed language,Text categorization,Decision tree learning | Conference | 2769 |
ISSN | Citations | PageRank |
0302-9743 | 41 | 2.67 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Núria Bel | 1 | 54 | 4.57 |
| Cornelis H. A. Koster | 2 | 281 | 57.53 |
| Marta Villegas | 3 | 72 | 13.44 |