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.
- 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), Sofia, Bulgaria, August 2013.
- Learning a Part-of-Speech Tagger from Two Hours of Annotation
[Details] [PDF]
Dan Garrette, Jason Baldridge
To Appear In Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13), June 2013.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments
[Details] [PDF]
Sugato Basu
PhD Thesis, University of Texas at Austin, 2005.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Semi-supervised Clustering: Learning with Limited User Feedback
[Details] [PDF]
Sugato Basu
Technical Report, Cornell University, 2004.
- Learnable Similarity Functions and Their Applications to Record Linkage and Clustering
[Details] [PDF]
Mikhail Bilenko
2003. Doctoral Dissertation Proposal, University of Texas at Austin.
- 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.
- 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.
