Difference between revisions of "Gimpel and Smith, NAACL 2010"
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==Summary== | ==Summary== | ||
| + | The authors want to be able to incorporate a cost function (present in structured SVMs) into standard conditional log-likelihood models. They introduce the softmax-margin objective function that achieves the best of both worlds. Using a NER task, it performs significantly better than a standard conditional loglikelihood model, a max-margin model, and the perceptron, but is indistinguishable from MIRA, risk, and JRB (Jensen risk bound; defined in the paper). | ||
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| + | ==Brief Description of the Softmax-Margin objective function== | ||
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| + | |||
| + | ==Experimental Results== | ||
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| + | ==Related Work== | ||
Revision as of 17:32, 25 September 2011
Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
Online: [1]
Contents
Citation
Kevin Gimpel and Noah A. Smith. Softmax-margin CRFs: Training log-linear models with loss functions. In Proceedings of the Human Language Technologies Conference of the North American Chapter of the Association for Computational Linguistics, pages 733-736, Los Angeles, California, USA, June 2010.
Summary
The authors want to be able to incorporate a cost function (present in structured SVMs) into standard conditional log-likelihood models. They introduce the softmax-margin objective function that achieves the best of both worlds. Using a NER task, it performs significantly better than a standard conditional loglikelihood model, a max-margin model, and the perceptron, but is indistinguishable from MIRA, risk, and JRB (Jensen risk bound; defined in the paper).
