Reconstructing DDC for interactive classification

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2007, Pages 137-146

  Wang, Jun ^a   Lee, Meng Chen ^a  

^a PEKING UNIVERSITY   (China)

Author keywords

Bibliographic data; Dewey decimal classification; Hierarchical classification; Interactive classification; Taxonomy reconstruction; Trimming machine

Indexed keywords

AUTOMATED TEXT CATEGORIZATIONS; AUTOMATIC CLASSIFICATIONS; BIBLIOGRAPHIC DATA; CLASSIFICATION PERFORMANCE; CLASSIFICATION SCHEMES; CLASSIFICATION SYSTEMS; DATA SPARSENESS; DEWEY DECIMAL CLASSIFICATION; HIERARCHICAL CLASSIFICATION; INTERACTIVE CLASSIFICATION; LIBRARY OF CONGRESS; NOVEL ALGORITHMS; REAL-WORLD APPLICATIONS; USER INTERACTIONS;

AUTOMATIC INDEXING; IMAGE QUALITY; KNOWLEDGE MANAGEMENT; RESTORATION; TAXONOMIES; TEXT PROCESSING; TRIMMING;

INFORMATION RETRIEVAL SYSTEMS;

EID: 63449099546     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1321440.1321462     Document Type: Conference Paper

References (27)

1. Akbani, R., Kwek, S., & Japkowicz, N. (2004). Applying support vector machines to imbalanced datasets.ECML'04. 39-50.
2. Bredensteiner, E. J., & Bennett, K. P. (1999). Multicategory classification by support vector machines. Computer optimization and applications, 12, 53-79.
3. Chan, L. M., Comaromi, J. P., Mitchell, J. S., & Satija, M. P. (1996). Dewey Decimal Classification: A Practical Guide, 2nd Edition. Albany, NY: Forest Press.
4. Chan, L.M. (2001). Exploiting LCSH, LCC, and DDC to retrieve networked resources: Issues and challenges. In Proceedings of the Bicentennial Conference on Bibliographic Control for the New Millennium (pp. 159-178), Washington, DC: Library of Congress.
5. Cai, L. & Hofmann, T. (2004). Hierarchical Document Categorization with Support Vector Machines, CIKM'04, November 8-13, 2004, Washington, DC, USA.
6. Dolin, R. A. (1998). Pharos: A scalable distributed architecture for locating heterogeneous information sources. Ph.D. thesis, University of California, Santa Barbara.
7. Dumais, S., & Chen, H. (2000). Hierarchical classification of Web content. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'00), (pp. 256-263).
8. Frank, E., & Paynter, G. W. (2004). Predicting Library of Congress Classifications from Library of Congress Subject Headings. Journal of the American Society for Information Science, 55 (3), 214-227.
9. Ganesan, P., Garcia-Molina, H., & Widom, J. (2003). Exploiting Hierarchical Domain Structure to Compute Similarity. ACM Transactions on Information Systems, 21(1), 64-93.
10. Gorman, M., & Winkler, P. W. (1998). Anglo-American cataloguing rules, 2d edition. Chicago, IL: American Library Association.
11. Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (n.d.). A Practical Guide to Support Vector Classification, from http://www.csie.ntu.edu.tw/~cjlin/ papers/guide/guide.pdf
12. Koller, D., & Sahami, M. (1997). Hierarchically classifying documents using very few words. ICML'97, (pp. 170-178).
13. Larson, R. R. (1992). Experiments in automatic Library of Congress Classification. Journal of the American Society for Information Science, 43 (2), 130-148.
14. Lewis, D. D. (1992). An evaluation of phrasal and clustered representations on a text categorization task. Proceedings of the 15th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR92), (pp. 37-50).
15. Library of Congress. (1990). LC Classification Outline (6th ed.). Washington, DC: Library of Congress.
16. Library of Congress acquisitions and bibliographic access directorates. Retrieved Jan. 26, 2007, from The Library of Congress: http://www.loc.gov
17. Library of Congress (1999). MARC 21 format for bibliographic data : including guidelines for content designation. Washington [D.C.]: Library of Congress.
18. Liu, T.-Y., Yang, Y., Wan, H., Zeng, H.-J., Chen, Z., & Ma, W.-Y. (2004). Support vector machines classification with a very large-scale taxonomy. SIGKDD Explorations, 36-43.
19. Mitchell, J. S. (1997). Abridged Dewey Decimal Classification and Relative Index, 13th Edition. Albany, NY: Forest Press.
20. OCLC. Dewey is the world's most widely used library classification system. Retrieved 1 26, 2007, from http://www.oclc.org/dewey/about/ translations/
21. Ruiz, M. E., & Srinivasan, P. (2002). Hierarchical text categorization using neural networks. Information Retrieval, 5(1), 87-118.
22. Schutze, H., Hull, D. A., & Pedersen, J. O. (1995). A comparison of classifiers and document representations for the routing problem. Proceedings of the 18 th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-95), (pp. 229-237).
23. Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34 (1), 1-47.
24. Sun, A., & Lim, E. (2001). Hierarchical Text classification and evaluation. ICDM 2001, (pp. 521-528).
25. Thompson, R., Shafer, K., & Vizine-Goetz, D. (1997). Evaluating Dewey concepts as a knowledge base for automatic subject assignment. Proceedings of the 2nd ACM international conference on Digital libraries, (pp. 37-46).
26. Weigend, A. S., Wiener, E. D., & Pedersen, J. O. (1999). Exploiting hierarchy in text categorization. Information Retrieval, 1 (3), 193-216.
27. Yang, Y., Zhang, J., & Kisiel, B. (2003). A scalability analysis of classifiers in text categorization. Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'03), (pp. 96-103).