D08-1112 describes in detail all the query selection strategies we consider . Section 4 presents
C04-1033 NP pairs according to a certain selection strategy . In this way , the identiflcation
C00-1020 context , therefore we need a rule selection strategy . Suppose one wants now to disambiguate
C04-1033 attempts to use such an instance selection strategy to incorporate the information
D08-1034 classifier based on this feature selection strategy . We will take this classifier
D10-1061 these ideas , we design a greedy selection strategy using the discriminative relational
C92-2099 COL1NG-92 , NAMES , AUG. 23-28 , 1992 selection strategy crossings rec ' , di precision
D08-1034 Classification with Hierarchical Feature Selection Strategy </title> Weiwei Baobao Abstract
D09-1116 investigate how parser accuracy and data selection strategies , e.g. , based on parser confidence
D10-1061 trigger . Algorithm 1 summarizes our selection strategy in pseudocode . Since each call
D10-1061 Eck et al. ( 2005 ) described a selection strategy that attempts to maximize coverage
D10-1061 Learning Traditionally , unsupervised selection strategies have dominated the active learning
C02-1150 may use in determining answer selection strategies that may be answer type specific
D10-1061 novel , discriminative sample selection strategy that preferentially selects batches
D08-1034 based on a hierarchical feature selection strategy . Different from the previous
D08-1112 We implement all fifteen query selection strategies described in Section 3 for use
D08-1112 We survey previously used query selection strategies for sequence models , and propose
D08-1034 further im - proved . 4 Feature Selection Strategy Due to what we have discussed
D08-1112 described and criticized the query selection strategies used with probabilistic se quence
D10-1007 input . To evaluate how well this selection strategy would work , we measured the
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