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ACL
2007

A Comparative Study of Parameter Estimation Methods for Statistical Natural Language Processing

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A Comparative Study of Parameter Estimation Methods for Statistical Natural Language Processing
This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. Then we apply the best of these estimators to two additional tasks involving conditional sequence models: a Conditional Markov Model (CMM) for part of speech tagging and a Conditional Random Field (CRF) for Chinese word segmentation. Our experiments show that across tasks, three of the estimators — ME estimation with L1 or L2 regularization, and the Averaged Perceptron — are in a near statistical tie ...
Jianfeng Gao, Galen Andrew, Mark Johnson, Kristina
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ACL
Authors Jianfeng Gao, Galen Andrew, Mark Johnson, Kristina Toutanova
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