Improvement of Feature Selection Algorithm in Maximum Entropy Model and Disambiguation of Error-Correction Candidates

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.