D12-1095 parser , usually employing a heavy pruning strategy . Then the goal of a forest reranker
D11-1109 We further present an effective pruning strategy to reduce the search space of
D09-1038 frequency rules are filtered out . The pruning strategy is similar to the cube pruning
D13-1161 the scoring function used by the pruning strategy to take advantage of all features
D11-1109 this section , we introduce two pruning strategies to constrain the search space
D11-1127 find that with carefully designed pruning strategies , HiFST can match the performance
A00-3002 multi-level chart parser with a radical pruning strategy to the captioning domain . 5
D13-1032 over POS candidates and apply our pruning strategy . In a second step we expand
D12-1076 shows that our co-occurrence based pruning strategy can help render the semantic
A00-3002 retained . Although the original pruning strategy resulted in many reasonable parses
D10-1004 both cases , we employed the same pruning strategy as Martins et al. ( 2009 ) .
D13-1022 called bit-strings . A common beam pruning strategy is to group together items into
C04-1059 and restricted by the applied pruning strategy . Ignoring word order , the hypothesis
A00-3002 parsing . As it was expected , the pruning strategy resulted in a significant reduction
D09-1123 is discarded . Therefore , this pruning strategy maintains only fully connected
D14-1042 cases , our syntactic and semantic pruning strategy increased performance ( up to
D09-1106 training corpus , such global pruning strategy usually leads to very large disk
D12-1044 able . We therefore use a hybrid pruning strategy : each word 's set of potential
A88-1013 data seem to indicate that this pruning strategy is not unreasonable , particularly
D09-1123 and thirdly , by modifying the pruning strategy to handle the large search space
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