C04-1057 data sets for which the adaptive greedy algorithm outperforms our baseline . This
C04-1057 Table 2 compares our modified greedy algorithm to the baseline . In that case
D09-1001 assigned to . Instead , USP uses a greedy algorithm to search for the MAP parse .
D08-1005 topicality scores . A simple , greedy algorithm for detecting Dk is then : 1
D08-1034 get the best one . So we use a greedy algorithm to get an approximate optimal
C96-1037 and distinctive t ~ atures . A A greedy algorithm for aligning words . The algorithm
C04-1152 Prefix Search The straightforward greedy algorithm schema for finding an approximately
C04-1024 proposed by Andreas Eisele1 . It is a greedy algorithm which tries to minimise the number
C04-1057 between the adaptive and modified greedy algorithm we found the latter to outperform
C94-2157 for developing and running his greedy algorithm . along with a measure corresponding
D08-1036 Following these authors , we used a greedy algorithm to associate states with POS
D09-1048 Iterative WSD IWSD is clearly a greedy algorithm . It bases its decisions on already
C94-2157 . The corpus wax run through a greedy algorithm 4 that returned the most frequent
D08-1079 sentences have been obtained , a greedy algorithm ( Wan and Yang , 2006 ) is applied
C96-1019 other type of generator applies a greedy algorithm to an initial solution in order
C04-1057 information and with the two variants of greedy algorithms described in Section 4 . We chose
C04-1057 Section 2.3 ) , we propose two greedy algorithms inspired by the algorithm above
C04-1057 NP-hard , properties of simple greedy algorithms have been explored , and a straightforward
C94-2156 dictionary data and corpora . A greedy algorithm 3 to locate the most common coocurrences
C04-1057 of the optimal solution . The greedy algorithm for maximum set coverage has
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