A97-1026 in ( 4 ) was then used for the rule generation process , described in the next
P01-1041 these facts , we modify the above rule generation . That is , we replace every
N04-2001 The results show that automatic rule generation with CART performs better than
D11-1047 additional treelets for onthe-fly rule generation in our simple MT system . Improvement
A97-1026 the purpose of concept grammar rule generation , each CSR from the training
A97-2019 information 5 . algorithms for automatic rule generation based on developer input . Natural
C65-1006 control class specialization and rule generation , we will use the following processing
A94-1019 into a logical framework where rule generation by metarules is calculated through
C65-1006 classi - fication , as part of the rule generation operation , will show how the
C04-1078 variation of the soft pattern rules generation and matching method presented
A97-1026 automated scoring process . Automated rule generation is significantly faster and more
A97-1026 concepts had to be removed before the rule generation process , so that the concept
D10-1060 mode . This is similar to the rule generation method in ( DeNeefe and Knight
P01-1041 decision tree learning . Brill 's rule generation method ( Brill , 2000 ) is not
C00-1046 ' over " ( verb ) ' . In this rule generation module , two important factors
C65-1006 and the ones following it for rule generation . Underlying this processing
E83-1006 for clashes before starting the rule generation process , as well as allow the
C90-2004 trivial . 3.3 . Phrase-structure rules generation In this last stage the phrase
E83-1006 is identical . In the course of rule generation , an inconsistency called a "
J03-4003 describes the use of Markov models for rule generation , which is closely related to
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