N07-1068 . For the language model , the Jelinek-Mercer smoothing method was employed with the
P11-1095 needs to tune one parameter : the Jelinek-Mercer smoothing parameter A used in the entity
W04-1104 be considered as an instance of Jelinek-Mercer smoothing . It is defined recursively as
P96-1041 This scheme is an instance of Jelinek-Mercer smoothing . Referring to equation ( 3 )
P11-1066 likelihood language model with Jelinek-Mercer smoothing can be D : ... for good cold
W09-2012 word to be predicted . A 5-gram Jelinek-Mercer smoothing language model on sentence x
W09-2012 Jelinek-Mercer " smoothing . As in Jelinek-Mercer smoothing ( Jelinek and Mercer , 1980 )
W04-0307 smooth the probabilities using Jelinek-Mercer smoothing ( Jelinek , 1997 ) , as described
W13-2504 techniques such as Backoff or Jelinek-Mercer smoothing , two techniques that generally
W13-2504 . We can also notice that the Jelinek-Mercer smoothing improves more notably the High-test
P96-1041 method , we use an instance of Jelinek-Mercer smoothing where we constrain all Ami-i
P96-1041 techniques , Katz smoothing and Jelinek-Mercer smoothing , perform consistently well across
P11-2005 1998 ) . We also compare against Jelinek-Mercer smoothing ( JMLM ) , which interpolates
W13-2504 = ( r + 1 ) Nr +1 ( 6 ) Nr 2.3 Jelinek-Mercer Smoothing As one alternative to missing
P96-1041 - niques , one a variation of Jelinek-Mercer smoothing and one a very simple linear
P14-2100 smoothing ( Ney et al. , 1995 ) , Jelinek-Mercer smoothing ( Jelinek and Mercer , 1980 )
P09-1082 et al. ( 2008 ) is that we use Jelinek-Mercer smoothing for equation 3 instead of Dirichlet
P96-1041 We implemented two versions of Jelinek-Mercer smoothing differing only in what data is
W13-2504 the Good-Turing estima - tions . Jelinek-Mercer smoothing counteracts the disadvantage
W07-0910 Lucene ( ILPS , 2005 ) and uses Jelinek-Mercer smoothing , controlled by the parameter
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