H93-1051 response \ -RSB- arc presented to a learning program . The program 's task is to devise
I05-2025 corpora . In section 6 , a machine learning program - C4 .5 is introduced . Section
A00-2028 experiments apply the machine learning program RIPPER ( Cohen , 1996 ) to automatically
I05-2025 non-native speakers . A machine learning program - C4 .5 was applied to induce
A97-1016 use a general symbolic machine learning program to acquire a decision tree for
C92-2085 more , the input corpora to such learning programs are often required to be properly
A00-2028 test sets , and 5 runs of the learning program are performed . Thus , each run
C92-2085 Japanese tagging program , Automatic Learning Program of Semantic Collocations and
A00-2028 used for utilizing the machine learning program RIPPER to train an automatic
D11-1014 supported in part by the DARPA Deep Learning program under contract number FA8650-10-C-7020
E97-1011 occurring data . We use a machine learning program , C4 .5 ( Quinlan , 1993 ) ,
C96-2149 5 Overview of the Learner Our learning program has two basic modules : IAm version
H01-1046 parser and language and concept learning programs . 1 . INTRODUCTION In natural
E99-1037 insight that traditional language learning programs do offer only few or none of
D09-1100 DARPA funding under the Bootstrap Learning Program and the Beckman Institute Postdoctoral
A00-2029 experiments using the machine learning program RIPPER ( Cohen , 1996 ) to automatically
D08-1037 DARPA funding under the Bootstrap Learning Program . <title> Studying the History
C04-1128 question detection . Like many learning programs , Ripper takes as input the classes
A00-2029 additional features . Like many learning programs , RIPPER takes as input the classes
D12-1110 - C-0181 , and the DARPA Deep Learning program under contract number FA8650-10-C-7020
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