Unsupervised search-based structured prediction

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 unsupervised 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.

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Hal Daumé III
• Keywords: ICML 2009, Machine Learning, Unsupervised Learning Problems, Unsupervised Searn, Unsupervised Shift-reduce Parsing