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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. (Update)
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BibTeX entry: (Update)
@misc{ lauser-automatic,
author = "Boris Lauser and Andreas Hotho",
title = "Automatic Multi-label Subject Indexing in a Multilingual Environment",
url = "citeseer.ist.psu.edu/lauser03automatic.html" }
Citations (may not include all citations):524 Support-Vector Networks - Cortes, Vapnik - 1995
376 Text categorization with Support Vector Machines: Learning w.. - Joachims - 1998
139 Machine learning in automated text categorization - Sebastiani - 1999
51 Uniform Resource Identifiers (context) - Berners-Lee, Fielding et al. - 1998
19 KAON -- Towards a Large Scale Semantic Web - Bozsak, Ehrig et al. - 2002
13 Multi-label text classification with a mixture model trained.. - McCallum - 1999
12 A new family of online algorithms for category ranking - Crammer, Singer - 2002
9 Practical Machine Learning Tools and techniques with Java im.. (context) - Witten, Frank - 1999
8 Text Categorization: a survey (context) - Aas, Eikvil - 1999
6 Automatic Subject Indexing Using an Associative Neural Netwo.. - Chung, Pottenger et al. - 1998
6 Text clustering based on background knowledge - Hotho, Staab et al. - 2003
3 Semi-automatic ontology engineering and ontology supported d.. (context) - Lauser - 2003
2 Akquisition von Ontologien mit Text-Mining-Verfahren (context) - Volz - 2000
1 Combining Machine Learning and Hierarchical Structures for t.. - Ruiz, Srinivasan - 1999
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