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《Transactions of Beijing Institute of Technology》 2006-01
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Improvement of Feature Selection Algorithm in Maximum Entropy Model and Disambiguation of Error-Correction Candidates

ZHANG Yang-sen~(1,3),CAO Yuan-da~2,YU Shi-wen~1 (1.Institute of Computational Linguistics,Peking University,Beijing 100871,China;2.School of Computer Software,Beijing Institute of Technology,Beijing 100081,China;3.Department of Computer and Automation,Beijing Information and Technology University,Beijing 100101,China)  
An improved feature selection algorithm in maximum entropy modeling approach is presented.Candidate feature set is acquired from the training sample corpus using templates,and the features are selected from the candidate feature set according to the combination of feature frequency and average mutual information.When selecting the effective feature,features in the candidate set whose frequency or average mutual information value is larger than a threshold are put into the effective feature set directly.The execution of parameter acquisition algorithm is not for each choice of feature,so the speed of feature selection is improved.The improved model is applied to sort the candidates of error-correction.The experiment shows that it has higher efficiency and precision.
【Fund】: 国家“九七三”计划项目(2004CB318102);; 国家“八六三”计划项目(2001AA114210 2002AA117010)
【CateGory Index】: TP391.1
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