C04-1057 theoretical results , including suitable approximation algorithms . Experiments using DUC data
C04-1030 reorderings or we have to use an approximation algorithm . Note that in the latter case
H90-1005 detail . However , to explain the approximation algorithm we will need to recall the main
D13-1040 1 i , r In every iteration the approximation algorithm randomly samples a term from
D13-1167 Cohen et al. ( 2013 ) proposed an approximation algorithm for PCFG parsing that relies
C88-2156 plane . However ~ the gradual approximation algorithm may try to push the object there
D10-1017 is NP-hard and so they propose approximation algorithms that are still quite complex
E97-1058 performed , just matching . ) 3 An approximation algorithm There are two main ideas behind
D14-1109 been shown to be NP-hard , many approximation algorithms work remarkably well . We commence
C04-1057 another . We compare each of our approximation algorithms ( adaptive and modified greedy
H90-1005 generates a regular language , the approximation algorithm yields an acceptor for that :
C88-2156 position suggested by the gradual approximation algorithm is ( x , y ) . ~ f L = x , ,
C04-1057 every version of our baseline and approximation algorithms , and separately for the tf *
E97-1058 } . The result of applying the approximation algorithm is a 3state automaton recognising
D15-1220 need for concept pruning using an approximation algorithm that achieves comparable performance
C04-1057 optimization task . We provide approximation algorithms and empirically validate the
E12-1023 monotonicity means that no efficient approximation algorithms are known for computing the highest-scoring
D14-1031 ) ( local MAP ) with two other approximation algorithms ( Gibbs Sampling and Particle
E06-1004 approach is to develop provably good approximation algorithms for these problems as is done
D15-1220 concept pruning by developing a fast approximation algorithm that achieves near-optimal performance
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