Many statistical parsers ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2001 ) are based on a history-based probability model ( Black et al. , 1993 ) , where the probability of each decision in a parse is conditioned on the previous decisions in the parse .
towards historybased grammars using richer models for probabilistic parsing
E Black F Jelinek J Lafferty D Magerman R Mercer S Roukos
Many statistical parsers ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2001 ) are based on a history-based probability model ( Black et al. , 1993 ) , where the probability of each decision in a parse is conditioned on the previous decisions in the parse . A major challenge in this approach is choosing a representation of the parse history from which the probability for the next parser decision can be accurately estimated .
Many statistical parsers ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2001 ) are based on a history-based probability model ( @@CITATION ) , where the probability of each decision in a parse is conditioned on the previous decisions in the parse .