Resources of Text Categorization

On Line Papers

• Overview and Feature selection
  1. David Dolan Lewis, Representation and Learning in Information Retrieval. PhD thesis, Department of Computer Science; Univ. of Massachusetts; Amherst, MA 01003, 1992.
  2. Yiming Yang and Xin Liu A re-examination of text categorization methods. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR), 1999.
  3. Yang, Y., Pedersen J.P. A Comparative Study on Feature Selection in Text Categorization Proceedings of the Fourteenth International Conference on Machine Learning (ICML’97), 1997.
• Support Vector Machines
  1. Thorsten Joachims , Text Categorization with Support Vector Machines: Learning with Many Relevant Features. European Conference on Machine Learning (ECML), Claire Nédellec and Céline Rouveirol (ed.), 1998.
  2. Robert Cooley , Classification of News Stories Using Support Vector Machines (1999). Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence Text Mining Workshop, August 1999.
  3. kernel-machines.org.
  4. S. Dumais and H. Chen, Hierarchical classification of Web content. Proceedings of SIGIR’00, August 2000, pp. 256-263.
• Naive Bayes
  1. Andrew McCallum and Kamal Nigam, A Comparison of Event Models for Naive Bayes Text Classification. AAAI-98 Workshop on “Learning for Text Categorization”
• K Nearest Neighbor
  1. Jerome H. Friedman , J. H. “Flexible Metric Nearest Neighbor Classification.” Technical Report (Nov. 1994).
• Decision Tree
  1. C. Apte ,F. Damerau, and S.M. Weiss, Text Mining with Decision Trees and Decision Rules, in Conference on Automated Learning and Discovery, Carnegie-Mellon University, June 1998.
  2. C. Apte , F. Damerau, and S.M. Weiss, Towards Language Independent Automated Learning of Text Categorization Models, in ACM SIGIR’94, July 1994.
  3. Robert E. Schapire and Yoram Singer, BoosTexter: A boosting-based system for text categorization. Machine Learning, to appear.
• Neural Network
• New Event Detection or Topic Detection
  1. J. Allan , J. Carbonell, G. Doddington, J. Yamron, and Y. Yang, “Topic Detection and Tracking Pilot Study: Final Report”. Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, pp. 194-218. (April 1998)
  2. J. Allan , R. Papka, and V. Lavrenko, “On-line New Event Detection and Tracking”, in SIGIR ‘98. (April 1998)
  3. Chris Clifton, Robert Cooley , TopCat: Data Mining for Topic Identification in a Text Corpus (1999). Proceedings of the 3rd European Conference of Principles and Practice of Knowledge Discovery in Databases, 1999.
• Hierarchical Categorization
  1. Doug Baker, Thomas Hofmann, Andrew McCallum and Yiming Yang, A Hierarchical Probabilistic Model for Novelty Detection in Text. Submitted to NIPS’99.
  2. Kamal Nigam, John Lafferty, Andrew McCallum , Using Maximum Entropy for Text Classification. IJCAI’99 Workshop on Information Filtering.
  3. Soumen Chakrabarti , Byron Dom, Rakesh Agrawal, and Prabhakar Raghavan, Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. International Journal on Very Large Data Bases, 7(3) pp163-178. Invited paper.
  4. K. Wang , S. Zhou, S.C. Liew, “Building hierarchical classifiers using class proximity”, VLDB 1999, September 1999, Edinburgh, UK, Morgan Kaufmann, 363-374.
  5. D. Koller and M. Sahami, Hierarchically classifying documents using very few words, . Proceedings of the 14th International Conference on Machine Learning (ICML), Nashville, Tennessee, July 1997, pages 170–178.

Machine Learning Resources

• David Aha’s list of machine learning resources

On Line Softwares

• SVM_LIGHT,By Thorsten JoachimsSupport Vector Machine
• Bow: A Toolkit for Statistical Language Modeling, Text Retrieval, Classification and Clustering, by Andrew McCallum. Naive Bayes approach
• K Nearest Neighbour Classifier, by CMU
• BAYDA 1.31 – Bayesian Predictive Discriminant Analysis, by Complex Systems Computation Group, Department of Computer Science, University of Helsinki

source:http://www.cs.helsinki.fi/group/doremi/categorization/categorylinks.html