Automatic Multi-label Subject Indexing in a Multilingual Environment
Identifieur interne : 000F36 ( Main/Merge ); précédent : 000F35; suivant : 000F37Automatic Multi-label Subject Indexing in a Multilingual Environment
Auteurs : Boris Lauser [Italie] ; Andreas Hotho [Allemagne]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2003.
Abstract
Abstract: This paper presents an approach to automatically subject index full-text documents with multiple labels based on binary support vector machines (SVM). The aim was to test the applicability of SVMs with a real world dataset. We have also explored the feasibility of incorporating multilingual background knowledge, as represented in thesauri or ontologies, into our text document representation for indexing purposes. The test set for our evaluations has been compiled from an extensive document base maintained by the Food and Agriculture Organization (FAO) of the United Nations (UN). Empirical results show that SVMs are a good method for automatic multi- label classification of documents in multiple languages.
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DOI: 10.1007/978-3-540-45175-4_14
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