P99-1037 words are simply retrieved by the memory-based learner from memory . Due to some ambiguity
H01-1052 Learner 1 corresponds to a trivial memory-based learner . This learner simply keeps track
N07-2045 ) Minnen et al. ( 2000 ) use a memory-based learner ( Daelemans et al. , 2000 ) to
E06-1030 spelling correction task , a simple memory-based learner trained on our Web Corpus achieved
P11-2055 tion . Although we also use a memory-based learner , our method is different from
P08-1027 , 2006 ) , decision trees and memory-based learners . Freely available tools like
E06-2021 learning algorithms , Tilburg Memory-Based Learner ( TiMBL ) 2 was used . In memory-based
P04-1040 achieved high accuracy using a memory-based learner . TiMBL performed well on tasks
C04-1010 constructed using the Tilburg Memory-Based Learner ( TiMBL ) ( Daele - mans et al.
P01-1005 words and/or parts of speech . The memory-based learner used only the word before and
E09-1051 guide . 3 Our Baseline Model The memory-based learner TiMBL ( Daelemans et al. , 2004
P01-1005 confusable pairs , excluding the memory-based learner . Voting was done by combining
P02-1055 , we used a variant of the IB1 memory-based learner and classifier as implemented
P03-2006 Quinlan , 1993 ) and the Tilburg Memory-Based Learner ( TiMBL ) ( Daelemans et al.
E06-1030 problem , we implemented the simple memory-based learner from Banko and Brill ( 2001 )
E06-1030 using the same algo - rithm . The memory-based learner was tested using the 18 confusion
P02-1055 learning curve experiments using a memory-based learner . Section 3 provides the experimental
P99-1037 new instances are presented to a memory-based learner , it searches for the bestmatching
N01-1015 learners ( Black et al. , 1998 ) , memory-based learners and hybrid symbolic approaches
D10-1079 tokens . They employ TiMBL , a memory-based learner , as their model and report an
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