D15-1163 accuracy and cover - age . We can do graph pruning simply by choosing to use different
D12-1076 decided by SCAN . The semantic graph pruning threshold is set to 0.27 tuned
W07-0721 experiment with several ways of graph pruning . Addition - ally , for each
N10-1108 redundant patterns are removed via a graph pruning algorithm . In experiments on
W07-0721 n-gram length greater than 5 . 5.4 Graph pruning The more complex is the reordering
W07-0721 , we experiment with different graph pruning which guarantees the translation
N12-1051 task-specific algorithms such as graph pruning , edge weighting , and so on
J13-3007 taxon - omy , we perform a step of graph pruning , as described in the next section
W07-0721 direc - tions ) . It is shown that graph pruning guarantees the efficiency of
S14-2034 pruning and packing algorithms . 3.1 Graph pruning Our PRUNING algorithm removes
D14-1034 where I stands for a preposition . Graph Pruning . The edge set of our model consists
W07-0721 efficiency : we analyze different graph pruning and we show the very low increase
W05-0834 systems are phrase-based . + Their graph pruning method is suboptimal as it considers
J13-3007 neither domain heuristics nor the graph pruning could completely eliminate the
S15-2155 between individual node pairs and graph pruning or edge collapsing ( Kozareva
W07-0721 have experimented with different graph pruning showing that best translation
S10-1094 . 4 Knowledge-Based WSD using Graph Pruning Wordnet can be viewed as a graph
D14-1088 described in Section 2.2.4 . Step 3 : Graph pruning ( line 18 ) The hypernym graph
J13-3007 regard to the two versions of our graph pruning algorithm , we found that TREE
J13-3007 heuristic rules , we devised a novel graph pruning algorithm , based on the Chu-Liu/Edmonds
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