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Title:Inference With Classifiers: A Study of Structured Output Problems in Natural Language Processing
Author(s):Punyakanok, Vasin
Doctoral Committee Chair(s):Roth, Dan
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Computer Science
Abstract:In this framework, we have shown the significance of incorporating constraints into the inference stage as a way to correct and improve the decisions of the stand alone classifiers. Although it is clear that incorporating constraints into inference necessarily improves global coherency, there is no guarantee of the improvement in the performance measured in terms of the accuracy of the local predictions---the metric that is of interest for most applications. We develop a better theoretic understanding of this issue. Under a reasonable assumption, we prove a sufficient condition to guarantee that using constraints cannot degrade the performance with respect to Hamming loss. In addition, we provide an experimental study suggesting that constraints can improve performance even when the sufficient conditions are not fully satisfied.
Issue Date:2005
Description:106 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.
Other Identifier(s):(MiAaPQ)AAI3202156
Date Available in IDEALS:2015-09-25
Date Deposited:2005

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