The choice of learning algorithm for each classifier is motivated by earlier findings showing that discriminative classifiers outperform other machine-learning methods on error correction tasks ( Rozovskaya and Roth , 2011 ) .
algorithm selection and model adaptation for esl correction tasks
A Rozovskaya D Roth
The other system components use the preprocessing tools only as part of candidate generation ( e.g. , to identify all nouns in the data for the noun classifier ) . The choice of learning algorithm for each classifier is motivated by earlier findings showing that discriminative classifiers outperform other machine-learning methods on error correction tasks ( Rozovskaya and Roth , 2011 ) . Thus , the classifiers trained on the learner data make use of a discriminative model .
The choice of learning algorithm for each classifier is motivated by earlier findings showing that discriminative classifiers outperform other machine-learning methods on error correction tasks ( @@CITATION ) .