In this paper , we extend two classes of model adaptation methods ( i.e. , model interpolation and error-driven learning ) , which have been well studied in statistical language modeling for speech and natural language applications ( e.g. , Bacchiani et al. , 2004 ; Bellegarda , 2004 ; Gao et al. , 2006 ) , to ranking models for Web search applications .
language model adaptation with map estimation and the perceptron algorithm
M Bacchiani B Roark M Saraclar
In this paper , we extend two classes of model adaptation methods ( i.e. , model interpolation and error-driven learning ) , which have been well studied in statistical language modeling for speech and natural language applications ( e.g. , Bacchiani et al. , 2004 ; Bellegarda , 2004 ; Gao et al. , 2006 ) , to ranking models for Web search applications . We have evaluated our methods on two adaptation experiments over a wide variety of datasets where the in-domain datasets bear different levels of similarities to their background datasets .
In this paper , we extend two classes of model adaptation methods ( i.e. , model interpolation and error-driven learning ) , which have been well studied in statistical language modeling for speech and natural language applications ( e.g. , @@CITATION ; Bellegarda , 2004 ; Gao et al. , 2006 ) , to ranking models for Web search applications .