UT ML Group: Publication: Text Categorization and Clustering

The ability to categorize natural-language documents and web pages into known categories using supervised learning or to cluster them into meaningful new categories using unsupervised learning has important applications in information retrieval, information filtering, knowledge management, and recommender systems. Our research has focused on applications of text learning to recommender systems and on semi-supervised clustering of documents.

Publications

  1. Spherical Topic Models [Abstract] [PDF]
    Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond Mooney
    To appear in NIPS'09 workshop: Applications for Topic Models: Text and Beyond

  2. Probabilistic Semi-Supervised Clustering with Constraints [Abstract] [PDF]
    Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney
    In Semi-Supervised Learning, O. Chapelle, B. Schoelkopf, and A. Zien (eds.), pp. 73-102, MIT Press, Cambridge, MA, 2006.

  3. Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments [Abstract] [PDF]
    Sugato Basu
    Ph.D. Thesis, Department of Computer Sciences, University of Texas at Austin, May, 2005.
    157 pages

  4. Model-based Overlapping Clustering [Abstract] [PDF]
    Banerjee, A., Krumpelman, C., Basu, S., Mooney, R.J., and Ghosh, J.
    Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, pp. 532--537, August 2005.

  5. A Probabilistic Framework for Semi-Supervised Clustering [Abstract] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    Best Research Paper Award
    Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), pp. 59-68, Seattle, WA, August 2004.

  6. Semi-supervised Clustering with Limited Background Knowledge [Abstract] [PDF]
    Sugato Basu
    Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, pp. 979-980, San Jose, CA, July 2004.

  7. Active Semi-Supervision for Pairwise Constrained Clustering [Abstract] [PDF]
    Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
    Proceedings of the SIAM International Conference on Data Mining (SDM-2004), pp. 333-344, Lake Buena Vista, FL, April 2004.

  8. Semi-supervised Clustering: Learning with Limited User Feedback [Abstract] [PDF]
    Sugato Basu
    Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin, November 2003.
    47 pages.
    Also appears as Technical Report UT-AI-TR-03-307, Artificial Intelligence Lab, University of Texas at Austin, January 2004.

  9. Semi-supervised Clustering by Seeding [Abstract] [PDF]
    Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
    Proceedings of the Nineteenth International Conference on Machine Learning (ICML-2002), pp. 19-26, Sydney, Australia, July 2002.

  10. Content-Based Book Recommending Using Learning for Text Categorization [Abstract] [PDF]
    Raymond J. Mooney and Loriene Roy
    Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, pp. 195-204, June 2000.

  11. Content-Based Book Recommending Using Learning for Text Categorization [Abstract] [PDF]
    Raymond J. Mooney and Loriene Roy
    Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.

  12. Using HTML Structure and Linked Pages to Improve Learning for Text Categorization [Abstract] [PDF]
    Michael B. Cline
    Undergraduate Honors Thesis, Department of Computer Sciences, University of Texas at Austin, May 1999.
    Also appears as Technical Report AI 98-270, Artificial Intelligence Lab, University of Texas at Austin.
    21 pages

  13. Book Recommending Using Text Categorization with Extracted Information [Abstract] [PDF]
    Raymond J. Mooney, Paul N. Bennett, and Loriene Roy
    The AAAI-98/ICML-98 Workshop on Learning for Text Categorization and the AAAI-98 Workshop on Recommender Systems, pp. 49-54, pp. 70-74, Madison, WI, July 1998.

  14. Text Categorization Through Probabilistic Learning: Applications to Recommender Systems [Abstract] [PDF]
    Paul N. Bennett
    Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin, May 1998.
    Also appears as Technical Report AI 98-270, Artificial Intelligence Lab, University of Texas at Austin.
    29 pages

mooney@cs.utexas.edu