Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are ll-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting.  also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization.  first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. Then  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.
['Jianfeng Gao', 'Galen Andrew', 'M. Johnson', 'Kristina Toutanova']
I am writing a research paper on a idea A Comparative Study of Parameter Estimation Methods for Statistical Natural Language Processing
[{"from": "human", "value": "I am writing a research paper on a idea A Comparative Study of Parameter Estimation Methods for Statistical Natural Language Processing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are ll-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting.  also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization.  first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. Then  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 \u2014 ME estimation with L1 or L2 regularization, and AP \u2014 are in a near statistical tie for first place."}]