%0 Conference Paper %B Proceedings of the 26th Annual International Conference on Machine Learning %D 2009 %T Unsupervised search-based structured prediction %A Daumé, Hal %X We describe an adaptation and application of a search-based structured prediction algorithm "Searn" to unsupervised learning problems. We show that it is possible to reduce unsupervised learning to supervised learning and demonstrate a high-quality un-supervised shift-reduce parsing model. We additionally show a close connection between unsupervised Searn and expectation maximization. Finally, we demonstrate the efficacy of a semi-supervised extension. The key idea that enables this is an application of the predict-self idea for unsupervised learning. %B Proceedings of the 26th Annual International Conference on Machine Learning %S ICML '09 %I ACM %C New York, NY, USA %P 209 - 216 %8 2009/// %@ 978-1-60558-516-1 %G eng %U http://doi.acm.org/10.1145/1553374.1553401 %R 10.1145/1553374.1553401