Abstract
Personalized classification refers to allowing users to define their own categories and automating the assignment of documents to these categories. In this paper, we examine the use of keywords to define personalized categories and propose the use of Support Vector Machine (SVM) to perform personalized classification. Two scenarios have been investigated. The first assumes that the personalized categories are defined in a flat category space. The second assumes that each personalized category is defined within a pre-defined general category that provides a more specific context for the personalized category. The training documents for personalized categories are obtained from a training document pool using a search engine and a set of keywords. Our experiments have delivered better classification results using the second scenario. We also conclude that the number of keywords used can be very small and increasing them does not always lead to better classification performance.
The work is partially supported by the SingAREN 21 research grant M48020004.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
T. Ault and Y. Yang. kNN at TREC-9. In Proc. of the 9th Text REtrieval Conference (TREC-9), Gaithersburg, Maryland, 2000.
S. T. Dumais and H. Chen. Hierarchical classification of Web content. In Proc. of the 23rd ACM Int. Conf. on Research and Development in Information Retrieval (SIGIR), pages 256–263, Athens, GR, 2000.
S. T. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In Proc. of the 7th Int. Conf. on Information and Knowledge Management, pages 148–155, 1998.
T. Joachims. SVM light, An implementation of Support Vector Machines (SVMs) in C. http://svmlight.joachims.org/.
T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Proc. of the 10th European Conf. on Machine Learning, pages 137–142, Chemnitz, DE, 1998.
D. Koller and M. Sahami. Hierarchically classifying documents using very few words. In Proc. of the 14th Int. Conf. on Machine Learning, pages 170–178, Nashville, US, 1997.
K.-S. Lee, J.-H. Oh, J. Huang, J.-H. Kim, and K.-S. Choi. TREC-9 experiments at KAIST: QA, CLIR and batch filtering. In Proc. of the 9th Text REtrieval Conference (TREC-9), Gaithersburg, Maryland, 2000.
A. K. McCallum. BOW: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/mccallum/bow, 1996.
D. Mladenic. Feature subset selection in text-learning. In Proc. of the 10th European Conf. on Machine Learning, pages 95–100, 1998.
D. W. Oard. The state of the art in text filtering. User Modeling and User-Adapted Interactions: An International Journal, 7(3):141–178, 1997.
M. J. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting web sites. Machine Learning, 27(3):313–331, 1997.
S. Robertson and D. A. Hull. The TREC-9 filtering track final report. In Proc. of the 9th Text REtrieval Conference (TREC-9), Gaithersburg, Maryland, 2000.
F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.
TREC. Text REtrieval Conference. http://trec.nist.gov/.
Y. Yang. An evaluation of statistical approaches to text categorization. Information Retrieval, 1(1–2):69–90, 1999.
Y. Yang and X. Liu. A re-examination of text categorization methods. In Proc. of the 22nd ACM Int. Conf. on Research and Development in Information Retrieval, pages 42–49, Berkeley, USA, Aug 1999.
Y. Zhang and J. Callan. YFilter at TREC-9. In Proc. of the 9th Text REtrieval Conference (TREC-9), Gaithersburg, Maryland, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, A., Lim, EP., Ng, WK. (2002). Personalized Classification for Keyword-Based Category Profiles. In: Agosti, M., Thanos, C. (eds) Research and Advanced Technology for Digital Libraries. ECDL 2002. Lecture Notes in Computer Science, vol 2458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45747-X_5
Download citation
DOI: https://doi.org/10.1007/3-540-45747-X_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44178-6
Online ISBN: 978-3-540-45747-3
eBook Packages: Springer Book Archive