Unsupervised Search-based Structured Prediction
Machine Learning
2009-06-30 v1
Abstract
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.
Cite
@article{arxiv.0906.5151,
title = {Unsupervised Search-based Structured Prediction},
author = {Hal Daumé},
journal= {arXiv preprint arXiv:0906.5151},
year = {2009}
}