E03-1053 length n is a key factor in ` n-gram language modeling . A context n that is too small
N06-1002 target , and tree order . Standard n-gram language modeling tools can be used to train MTU
D14-1158 algorithm with robust performance in n-gram language modeling . KN smoothing defines alternate
E09-1004 were used to approximate standard n-gram language modeling ( LM ) . In fact , we did experiment
N04-4034 history mapping . The case of n-gram language modeling , where ( ht ) = wt − n
N09-3004 efficient techniques based on n-gram Language modeling . We evaluated the models by
N03-1025 categorization based on character level n-gram language modeling . The approach is evaluated on
N07-1055 and Lee , 2004 ) , which uses n-gram language modeling . It also uses a model of lexical
P11-1027 of keys and values needed for n-gram language modeling , generic implementations do
P06-2040 Capability Recent progress in variable n-gram language modeling has provided an efficient representation
N04-4007 shown in Table 1 ( C ) . Dynamic n-gram Language Modeling : During story reading we can
D14-1175 log-bilinear model proposed for n-gram language modeling in ( Mnih and Hinton , 2007 )
N04-4007 history information and dynamic n-gram language modeling . By additionally incorporating
P12-3004 ( Galley et al. , 2006 ) . 3.3 N-gram Language Modeling The toolkit includes a simple
N04-1005 derive some of the benefits of N-gram language modeling techniques . This technique is
J09-3002 derive some of the benefits of n-gram language modeling techniques . Similar approaches
D14-1158 PLRE ) , a flexible framework for n-gram language modeling where ensembles of low rank matrices
P12-3011 finite-state transducers , and for n-gram language modeling . The OpenGrm libraries use the
P05-1064 Phone Recognition followed by n-gram Language Modeling , or PRLM ( Zissman , 1996 )
C02-1096 word prediction . 1 Introduction N-gram language modeling techniques have been successfully
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