Abstract. This paper presents an approach to automatically subject index fulltext 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.
Lauser and Hotho REF indexed full-text documents in the agricultural domain with binary support vector machines (SVM).
Automatic Multi-label Subject Indexing in a Multilingual Environment
ECDL
Computer Science