Steven P. Crain
ABSTRACT
In this paper, we discuss our work in progress towards a scalable hierarchical classification system for books using the Library of Congress subject hierarchy. We examine the characteristics of this domain which make the problem very challenging, and we look at several appropriate performance measurements. We show that both Hieron and Hierarchical Support Vector Machines perform moderately well.
- T. Betts, M. Milosavljevic, and J. Oberlander. The utility of information extraction in the classification of books. In Proceedings of ECIR, 2007. Google Scholar
Digital Library
- O. Dekel, J. Keshet, and Y. Singer. Large margin hierarchical classification. In Proc. of 21st International Conference on Machine Learning (ICML), 2004. Google ScholarDigital Library
- I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In Proc. of 21st Int'l Conf. on Machine Learning (ICML), 2004. Google ScholarDigital Library
Index Terms
- A scalable assistant librarian: hierarchical subject classification of books
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