The standard way to handle this problem is to handcraft a finite set of features which provides a sufficient summary of the unbounded history ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2000 ) .
headdriven statistical models for natural language parsing
Michael Collins
Most probability estimation methods require that there be a finite set of features on which the probability is conditioned . The standard way to handle this problem is to handcraft a finite set of features which provides a sufficient summary of the unbounded history ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2000 ) . The probabilities are then assumed to be independent of all the infoimation about the history which is not captured by the chosen features .
The standard way to handle this problem is to handcraft a finite set of features which provides a sufficient summary of the unbounded history ( Ratnaparkhi , 1999 ; @@CITATION ; Charniak , 2000 ) .