A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing
Name
Rush-2012-A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing.pdf
Size
490.47 KB
Format
Adobe PDF
Checksum (MD5)
111d5fd2eb37f123d3fbf792764aa106
Author(s)
Rush, Alexander Matthew
Date Issued
October 2012
Journal
Journal of Artificial Intelligence Research
Publisher
Association for the Advancement of Artificial Intelligence
Citation
A. M. Rush and M. J. Collins (2012) "A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing", Volume 45, pages 305-362. © Copyright 2012 AI Access Foundation, Inc.
Version
Final published version
Abstract
Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, describe formal guarantees for the method, and describe practical issues in implementing the algorithms. While our examples are predominantly drawn from the NLP literature, the material should be of general relevance to inference problems in machine learning. A central theme of this tutorial is that Lagrangian relaxation is naturally applied in conjunction with a broad class of combinatorial algorithms, allowing inference in models that go significantly beyond previous work on Lagrangian relaxation for inference in graphical models.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1613/jair.3680
