A statistical technique which has recently become popular for NLP is Maximum Entropy/Minimum Divergence ( MEMD ) modeling ( Berger et al. , 1996 ) .
a maximum entropy approach to natural language processing
Adam L Berger Stephen A Della Pietra Vincent J Della Pietra
Much recent research in SMT , eg ( Garcia-Varea et al. , 1998 ; Niessen et al. , 1998 ; Och et al. , 1999 ; Wang and Waibel , 1998 ) deals with the decoding problem , either directly or indirectly because of constraints imposed on the form of the translation model . A statistical technique which has recently become popular for NLP is Maximum Entropy/Minimum Divergence ( MEMD ) modeling ( Berger et al. , 1996 ) . One of the main strengths of MEMD is that it allows information from different sources to be combined in a principled and effective way , so it is a natural choice for modeling p ( wlh , s ) In this paper , I describe a MEMD model for p ( wlh , s ) and compare its performance to that of an equivalent linear model .
A statistical technique which has recently become popular for NLP is Maximum Entropy/Minimum Divergence ( MEMD ) modeling ( @@CITATION ) .