Large-Margin Structured Prediction via Linear Programming

Zhuoran Wang, John Shawe-Taylor
Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:599-606, 2009.

Abstract

This paper presents a novel learning algorithm for structured classification problems, where the task is to predict multiple and interacting labels (multilabel) for an input object. The maximum margin separation between the correct multilabels and the incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of the column generation and the extragradient method for linear programming to gain further efficiency. Compared to previous works on large-margin structured prediction, this framework has advantages in handling arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of the proposed approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v5-wang09d, title = {Large-Margin Structured Prediction via Linear Programming}, author = {Wang, Zhuoran and Shawe-Taylor, John}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {599--606}, year = {2009}, editor = {van Dyk, David and Welling, Max}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/wang09d/wang09d.pdf}, url = {https://proceedings.mlr.press/v5/wang09d.html}, abstract = {This paper presents a novel learning algorithm for structured classification problems, where the task is to predict multiple and interacting labels (multilabel) for an input object. The maximum margin separation between the correct multilabels and the incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of the column generation and the extragradient method for linear programming to gain further efficiency. Compared to previous works on large-margin structured prediction, this framework has advantages in handling arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of the proposed approach.} }
Endnote
%0 Conference Paper %T Large-Margin Structured Prediction via Linear Programming %A Zhuoran Wang %A John Shawe-Taylor %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-wang09d %I PMLR %P 599--606 %U https://proceedings.mlr.press/v5/wang09d.html %V 5 %X This paper presents a novel learning algorithm for structured classification problems, where the task is to predict multiple and interacting labels (multilabel) for an input object. The maximum margin separation between the correct multilabels and the incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of the column generation and the extragradient method for linear programming to gain further efficiency. Compared to previous works on large-margin structured prediction, this framework has advantages in handling arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of the proposed approach.
RIS
TY - CPAPER TI - Large-Margin Structured Prediction via Linear Programming AU - Zhuoran Wang AU - John Shawe-Taylor BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-wang09d PB - PMLR DP - Proceedings of Machine Learning Research VL - 5 SP - 599 EP - 606 L1 - http://proceedings.mlr.press/v5/wang09d/wang09d.pdf UR - https://proceedings.mlr.press/v5/wang09d.html AB - This paper presents a novel learning algorithm for structured classification problems, where the task is to predict multiple and interacting labels (multilabel) for an input object. The maximum margin separation between the correct multilabels and the incorrect ones is formulated as a linear program. Instead of explicitly writing out the entire problem with an exponentially large constraint set, the linear program is solved iteratively via column generation. In this case, the process of generating most violated constraints is equivalent to searching for highest-scored misclassified incorrect multilabels, which can be easily achieved by decoding the structure based on current estimations. In addition, we also explore the integration of the column generation and the extragradient method for linear programming to gain further efficiency. Compared to previous works on large-margin structured prediction, this framework has advantages in handling arbitrary structures and larger-scale problems. Experimental results on part-of-speech tagging and statistical machine translation tasks are reported, demonstrating the competitiveness of the proposed approach. ER -
APA
Wang, Z. & Shawe-Taylor, J.. (2009). Large-Margin Structured Prediction via Linear Programming. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 5:599-606 Available from https://proceedings.mlr.press/v5/wang09d.html.

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