A scalable assistant librarian: hierarchical subject classification of books

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.

REFERENCES

• 1: T. Betts, M. Milosavljevic, and J. Oberlander. The utility of information extraction in the classification of books. In Proceedings of ECIR, 2007.
• 2: Ofer Dekel , Joseph Keshet , Yoram Singer, Large margin hierarchical classification, Proceedings of the twenty-first international conference on Machine learning, p.27, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015374]
• 3: Ioannis Tsochantaridis , Thomas Hofmann , Thorsten Joachims , Yasemin Altun, Support vector machine learning for interdependent and structured output spaces, Proceedings of the twenty-first international conference on Machine learning, p.104, July 04-08, 2004, Banff, Alberta, Canada [doi>10.1145/1015330.1015341]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.6 Library Automation
          Subjects: Large text archives

General Terms:
Experimentation, Performance

Keywords:
hierarchical classification, hieron, support vector machine