Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Semi-Supervised Learning

In many learning tasks, there is a large supply of unlabeled data but insufficient labeled data since it can be expensive to generate. Semi-supervised learning combines labeled and unlabeled data during training to improve performance. Semi-supervised learning is applicable to both classification and clustering. In supervised classification, there is a known, fixed set of categories and category-labeled training data is used to induce a classification function. In semi-supervised classification, training also exploits additional unlabeled data, frequently resulting in a more accurate classification function. In semi-supervised clustering, some labeled data is used along with the unlabeled data to obtain a better clustering.
  1. Weakly-Supervised Bayesian Learning of a CCG Supertagger
    [Details] [PDF] [Slides] [Poster]
    Dan Garrette and Chris Dyer and Jason Baldridge and Noah A. Smith
    In Proceedings of the Eighteenth Conference on Computational Natural Language Learning (CoNLL-2014), 141--150, Baltimore, MD, June 2014.
  2. Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages
    [Details] [PDF]
    Dan Garrette and Jason Mielens and Jason Baldridge
    To Appear In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), 583--592, Sofia, Bulgaria, August 2013.
  3. Learning a Part-of-Speech Tagger from Two Hours of Annotation
    [Details] [PDF] [Slides] [Video]
    Dan Garrette, Jason Baldridge
    In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13), 138--147, Atlanta, GA, June 2013.
  4. Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries
    [Details] [PDF]
    Dan Garrette and Jason Baldridge
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), 821--831, Jeju, Korea, July 2012.
  5. Semi-supervised graph clustering: a kernel approach
    [Details] [PDF]
    Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney
    Machine Learning Journal, 74(1):1-22, 2009.
  6. Watch, Listen & Learn: Co-training on Captioned Images and Videos
    [Details] [PDF]
    Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney
    In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 457--472, Antwerp Belgium, September 2008.
  7. Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    [Details] [PDF] [Slides]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), 81--84, Rochester, NY, April 2007.
  8. Learnable Similarity Functions and Their Application to Record Linkage and Clustering
    [Details] [PDF]
    Mikhail Bilenko
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2006. 136 pages.
  9. Probabilistic Semi-Supervised Clustering with Constraints
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney
    In O. Chapelle and B. Sch{"{o}}lkopf and A. Zien, editors, Semi-Supervised Learning, Cambridge, MA, 2006. MIT Press.
  10. Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments
    [Details] [PDF]
    Sugato Basu
    PhD Thesis, University of Texas at Austin, 2005.
  11. Semi-supervised Graph Clustering: A Kernel Approach
    [Details] [PDF]
    B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney
    In Proceedings of the 22nd International Conference on Machine Learning, 457--464, Bonn, Germany, August 2005. (Distinguished Student Paper Award).
  12. A Probabilistic Framework for Semi-Supervised Clustering
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), 59-68, Seattle, WA, August 2004.
  13. Semi-supervised Clustering with Limited Background Knowledge
    [Details] [PDF]
    Sugato Basu
    In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, 979--980, San Jose, CA, July 2004.
  14. Learnable Similarity Functions and Their Applications to Clustering and Record Linkage
    [Details] [PDF]
    Mikhail Bilenko
    In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, 981--982, San Jose, CA, July 2004.
  15. Integrating Constraints and Metric Learning in Semi-Supervised Clustering
    [Details] [PDF]
    Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney
    In Proceedings of 21st International Conference on Machine Learning (ICML-2004), 81-88, Banff, Canada, July 2004.
  16. A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields
    [Details] [PDF]
    Mikhail Bilenko and Sugato Basu
    In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.
  17. Active Semi-Supervision for Pairwise Constrained Clustering
    [Details] [PDF]
    Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
    In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04), April 2004.
  18. Semisupervised Clustering for Intelligent User Management
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    In Proceedings of the IBM Austin Center for Advanced Studies 5th Annual Austin CAS Conference, Austin, TX, February 2004.
  19. Semi-supervised Clustering: Learning with Limited User Feedback
    [Details] [PDF]
    Sugato Basu
    Technical Report, Cornell University, 2004.
  20. Learnable Similarity Functions and Their Applications to Record Linkage and Clustering
    [Details] [PDF]
    Mikhail Bilenko
    2003. Doctoral Dissertation Proposal, University of Texas at Austin.
  21. Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering
    [Details] [PDF]
    Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
    In Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, 42-49, Washington, DC, 2003.
  22. Semi-supervised Clustering by Seeding
    [Details] [PDF]
    Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
    In Proceedings of 19th International Conference on Machine Learning (ICML-2002), 19-26, 2002.