2The algorithm was implemented by the the authors , following the description in Hepple ( 2000 ) .
independence and commitment assumptions for rapid training and execution of rulebased pos taggers
M Hepple
t , and C [ s ] = T [ s ] g the samples on which the rule applies and changes the classification from correct to incorrect ; similarly , bad ( r ) = jB ( r ) j. Given a newly learned rule b that is to be applied to S , the goal is to identify the rules r for which at least one of the sets G ( r ) , B ( r ) is modified by the application of rule b. Obviously , if both sets are not modified when applying rule b , then the value of the objective function for rule r remains unchanged . 2The algorithm was implemented by the the authors , following the description in Hepple ( 2000 ) . The presentation is complicated by the fact that , in many NLP tasks , the samples are not independent .
2The algorithm was implemented by the the authors , following the description in @@CITATION .