E14-4034 considered derivations during k-best parsing . Given two derivations with
D13-1064 these translations . Incorporating K-Best parsing into our pipeline may help mitigate
D09-1059 parses , not only the best one . In k-best parsing , we maintain a k-best list in
E14-4034 . This is done with a top-down k-best parsing algorithm . Finally , the translation
J15-3002 document-level ; Table 8 shows the k-best parsing results of TSP 1S-1S on the RST
P09-1108 side . We examine the cost of k-best parsing in the source side of such grammars
P09-1108 . Since the bottom-up pass of k-best parsing is the bottleneck , we also examine
P09-1108 time spent in the 1-best phase of k-best parsing . As a base - line , we compared
P08-1023 forest-based algorithms based on k-best parsing ( Huang and Chiang , 2005 ) .
P10-1033 found to be well handled by the K-Best parsing method in Huang and Chiang (
P07-1019 approaches for this problem based on k-best parsing algorithms and demonstrate their
P11-1125 efficient forest-based algorithms for k-best parsing ( Huang and Chiang , 2005 ) .
C96-2185 ranking the parse trees to get k-best parsing re - sults . Its current accuracy
P14-2107 monotonicity property . Based on it , k-best parsing merges k-best subtrees in the
P08-1067 derivations at each node , and uses the k-best parsing Algorithm 2 of Huang and Chiang
P07-1019 items at each node , and uses the k-best parsing Algorithm 2 of Huang and Chiang
J15-3002 test set as a function of k of k-best parsing . The 1-best result tells that
P12-1064 approximate algorithms . In terms of k-best parsing , Huang and Chiang ( 2005 ) proposed
N06-3004 large . Previous algorithms for k-best parsing ( Collins , 2000 ; Charniak and
P09-1108 efficient known algorithm for k-best parsing ( Jim ´ enez and Marzal
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