W06-2904 two ideas for improving large margin training in the context of dependency
N07-3002 approach , I improve structured large margin training for parsing in two ways ( Wang
D12-1051 chunks . A relaxed , online maximum margin training algorithm is used for learning
P08-1061 semi-supervised structured large margin training . Unlike previous proposed approaches
N07-3002 currently being used in large margin training algorithms . In the second approach
P08-1061 proposed semi-supervised large margin training algorithm outperforms the supervised
W06-2904 discussing our modifications to large margin training for parsing in detail , we first
N12-1013 softmax-margin . MLE and softmax - margin training were statistically indistinguishable
P08-1061 the supervised structured large margin training approach in the experiments in
W06-2904 for future research . 4 Large Margin Training Given a training set of sentences
N12-1023 al. , 2006 ) , an online large - margin training algorithm . It has recently shown
W06-2904 give a brief overview of large margin training , and then present our two modifications
J14-4004 usual in such methods , the large margin training improves the quality of the learned
D10-1059 investigate a perceptron-like online margin training for statisit - ical machine translation
N07-3002 appears to have culminated in large margin training approaches ( Taskar et al. ,
N07-3002 not being used in current large margin training algorithms . Another unexploited
P08-1061 semi-supervised structured large margin training , and an efficient training algorithm
P08-1061 application of the structured large margin training approach first proposed in (
D12-1051 is a relaxed , online maximum margin training algorithm with the desired accuracy
P08-1061 Training Supervised structured large margin training approaches have been applied
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