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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. . | A Comparative Study of Parameter Estimation Methods for Statistical Natural Language Processing Jianfeng Gao Galen Andrew Mark Johnson Kristina Toutanova Microsoft Research Redmond WA 98052 jfgao galena kristout @microsoft.com Brown University Providence RI 02912 mj@cs.brown. edu Abstract 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 AP are in a near statistical tie for first place. 1 Introduction Parameter estimation is fundamental to many statistical approaches to NLP. Because of the high-dimensional nature of natural language it is often easy to generate an extremely large number of features. The challenge of parameter estimation is to find a combination of the typically noisy redundant features that accurately predicts the target output variable and avoids overfitting. Intuitively this can be achieved either by selecting a small number of highly-effective features and ignoring the others or by averaging over a large number of weakly informative features. The first intuition motivates feature selection methods such as Boosting and BLasso e.g. Collins 2000 Zhao and Yu 2004 which usually work best when many .
