C04-1127 pattern candidates ordered by the ranking function . The resulting performance is
C04-1202 the explicit form of sentence ranking functions for the automatic text summarization
C04-1199 reimplementation does not use a ranking function ( Radev et al. , 2000 ) . When
D08-1018 We discuss the details of the ranking function f used to compute the utility
D08-1015 retrieval . These techniques generate ranking functions which predict the relevance of
D08-1015 . First , all of the pairwise ranking functions are applied to the unseen document
D08-1018 Section 5.3 . Using utility as the ranking function , we sort all pairs of symbols
C04-1202 learning mechanism for sentence ranking function . In the preliminary experiments
D08-1018 be written as O ( Nkn3 ) . 5.3 Ranking function We discuss the details of the
D08-1015 the M ( M -- 1 ) / 2 pairwise ranking functions gjk created during the training
C04-1202 standard deviation . 2.3 Sentence ranking function We assume that for a certain
D08-1018 determine the better form of the ranking function f as well as to tune its weights
C04-1202 for an ideal uniform sentence ranking function . 6 Acknowledgements Our thanks
D09-1068 most important feature sets in ranking functions . Traditional proximity features
D09-1055 associations into a search engine 's ranking function should help improve both search
C04-1143 length constraints . The following ranking function rank ( j ) , where j is the sentence
D09-1068 approach to web search is to learn a ranking function h ( xi ) , where xi is a feature
C04-1202 is scored based on a sentence ranking function constructed by GEP . Fitness
D08-1111 is w * , then we can make the ranking function as f ( x ) = ( w * , x ) . When
C04-1202 the current candidate sentence ranking function under consideration : ) ) , (
hide detail